Finding linguistic patterns using spaCy

This section teaches you to find linguistic patterns using spaCy, a natural language processing library for Python.

If you are unfamiliar with the linguistic annotations produced by spaCy or need to refresh your memory, revisit Part II before working through this section.

After reading this section, you should:

  • know how to search for patterns based on part-of-speech tags and morphological features

  • know how to search for patterns based on syntactic dependencies

  • know how to examine the matching patterns in their context of occurrence

Finding patterns using spaCy Matchers

Linguistic annotations, such as part-of-speech tags, syntactic dependencies and morphological features, help impose structure on written language. Crucially, linguistic annotations allow searching for structural patterns instead of individual words or phrases. This allows defining search patterns in a flexible way.

In the spaCy library, the capability for pattern search is provided by various components named Matchers.

spaCy provides three types of Matchers:

  1. A Matcher, which allows defining rules that search for particular words or phrases by examining Token attributes.

  2. A DependencyMatcher, which allows searching parse trees for syntactic patterns.

  3. A PhraseMatcher, a fast method for matching spaCy Doc objects to Doc objects.

The following sections show you how to use the Matcher for matching Tokens and their sequences based on their part-of-speech tags and morphological features, and how to use the DependencyMatcher for matching syntactic dependencies.

Matching words or phrases

To get started with the Matcher, let’s import the spaCy library and load a small language model for English.

# Import the spaCy library into Python
import spacy

# Load a small language model for English; assign the result under 'nlp'
nlp = spacy.load('en_core_web_sm')

To have some data to work with, let’s load some text from a Wikipedia article.

To do so, we use Python’s open() function in combination with the with statement to open the file for reading, providing the file, mode and encoding arguments, as instructed in Part II.

We then call the read() method to read the file contents and store the result under the variable text.

# Use the open() function with the 'with' statement to open the file for reading
with open(file='data/occupy.txt', mode='r', encoding='utf-8') as file:
    
    # Use the read() method to read the contents of the file; assign result under 
    # the variable 'text'
    text = file.read()

This returns a Python string object that contains the article in plain text, which is now available under the variable text.

Next, we then feed this object to the language model under the variable nlp as instructed in Part II.

We also use Python’s len() function to count the number of words in the text.

# Feed the string object to the language model
doc = nlp(text)

# Use the len() function to check length of the Doc object to count 
# how many Tokens are contained within the Doc.
len(doc)
14867

Now that we have a spaCy Doc object with nearly 15 000 Tokens, we can continue to import the Matcher class from the matcher submodule of spaCy.

# Import the Matcher class
from spacy.matcher import Matcher

Importing the Matcher class from spaCy’s matcher submodule allows creating Matcher objects.

When creating a Matcher object, you must provide the vocabulary of the language model used for finding matches to the Matcher object.

The reason for this is really rather simple: if you want to search for patterns in some language, you need to know its vocabulary first.

spaCy stores the vocabulary of a model in a Vocab object. The Vocab object can be found under the attribute vocab of a spaCy Language object, which was introduced in Part II.

In this case, we have the Language object that contains a small language model for English stored under the variable nlp, which means we can access its Vocab object under the attribute nlp.vocab.

We then call the Matcher class and provide the vocabulary under nlp.vocab to the vocab argument to create a Matcher object. We store the resulting object under the variable matcher.

# Create a Matcher and provide model vocabulary; assign result under the variable 'matcher'
matcher = Matcher(vocab=nlp.vocab)

# Call the variable to examine the object
matcher
<spacy.matcher.matcher.Matcher at 0x1730d26c0>

The Matcher object is now ready to store the patterns that we want to search for.

These patterns, or more specifically, pattern rules, are created using a specific format defined in spaCy.

Each pattern consists of a Python list, which is populated by Python dictionaries.

Each dictionary in this list describes the pattern for matching a single spaCy Token.

If you wish to match a sequence of Tokens, you must define multiple dictionaries within a single list, whose order follows that of the pattern to be matched.

Let’s start by defining a simple pattern for matching sequences of pronouns and verbs, and store this pattern under the variable pronoun_verb.

This pattern consists of a list, as marked by the surrounding brackets [], which contains two dictionaries, marked by curly braces {} and separated by a comma. The key and value pairs in a dictionary are separated by a colon.

  • The dictionary key determines which Token attribute should be searched for matches. The attributes supported by the Matcher can be found here.

  • The value under the dictionary key determines the specific value for the attribute.

In this case, we define a pattern that searches for a sequence of two coarse part-of-speech tags (POS), which were introduced in Part II, namely pronouns (PRON) and verbs (VERB).

Note that both keys and values must be provided in uppercase letters.

# Define a list with nested dictionaries that contains the pattern to be matched
pronoun_verb = [{'POS': 'PRON'}, {'POS': 'VERB'}]

Now that we have defined the pattern using a list and dictionaries, we can add it to the Matcher object under the variable matcher.

This can be achieved using add() method, which requires two inputs:

  1. A Python string object that defines a name for the pattern. This is required for purposes of identification.

  2. A list containing the pattern(s) to be searched for. Because a single rule for matching patterns can contain multiple patterns, the input must be a list of lists. We therefore wrap the input lists into brackets, e.g. [pattern_1].

# Add the pattern to the matcher under the name 'pronoun+verb'
matcher.add("pronoun+verb", patterns=[pronoun_verb])

To search for matches in the Doc object stored under the variable doc, we feed the Doc object to the Matcher and store the result under the variable result.

We also set the optional argument as_spans to True, which instructs spaCy to return the results as Span objects.

As you may remember from Part II, Span objects correspond to continuous “slices” of Doc objects.

# Apply the Matcher to the Doc object under 'doc'; provide the argument
# 'as_spans' and set its value to True to get Spans as output
result = matcher(doc, as_spans=True)

# Call the variable to examine the output
result
[that expressed,
 It aimed,
 It formed,
 this began,
 it organizes,
 that read,
 who designed,
 He wrote,
 there were,
 They promoted,
 It refers,
 which started,
 that indicate,
 that allowed,
 they saw,
 they argued,
 they called,
 it takes,
 that reflected,
 that strip,
 they called,
 that took,
 who comment,
 them using,
 they belong,
 which premiered,
 himself warned,
 he said,
 they think,
 them gain,
 they wished,
 that saw,
 there was,
 they blamed,
 I support,
 Some believe,
 that followed,
 It showed,
 Some find,
 Some believe,
 there was,
 which involves,
 which showed,
 who gave,
 Some said,
 they refused,
 they saw,
 who caused,
 that drew,
 there were,
 who sought,
 there were,
 that lasted,
 There was,
 This came,
 which shut,
 who made,
 that took,
 This included,
 which prohibit,
 that hosted,
 which monitors,
 those wishing,
 who criticized,
 it returned,
 its proposed,
 They received,
 that advocates,
 there have,
 There are,
 it came,
 it gained,
 He claimed,
 they presented,
 which took,
 they call,
 that started,
 which saw,
 all set,
 there were,
 It consists,
 there were,
 there were,
 which started,
 there was,
 which featured,
 all finding,
 What started,
 there was,
 There was,
 Some said,
 they began,
 they perceived,
 that threaten,
 they say,
 We agree,
 we see,
 There's,
 There are,
 who say,
 they do,
 they reflect,
 He mentioned,
 We regard,
 who participated,
 he wrote,
 we have,
 who dislike,
 that burdens,
 they employ,
 they have,
 there were,
 that differs,
 that follow,
 there is,
 that abstract,
 it stall,
 who emerged,
 It pushes,
 who called,
 that deals,
 that believe]

The output is a list of spaCy Span objects that match the requested pattern. Let’s examine the first object in the list of matches in greater detail.

result[0]
that expressed

The Span object has various useful attributes, including start and end. These attributes contain the indices that indicate where in the Doc object the Span starts and finishes.

result[0].start, result[0].end
(14, 16)

Another useful attribute is label, which contains the name that we gave to the pattern. Let’s take a closer look at this attribute.

result[0].label
12298179334642351811

The number stored under the label attribute is actually a spaCy Lexeme object that corresponds to an entry in the language model’s vocabulary.

This Lexeme contains the name that we gave to the search pattern above, namely pronoun+verb.

We can easily verify this by using the value under result[0].label to fetch the Lexeme from the Vocab object under nlp.vocab and examining its text attribute.

# Access the model vocabulary using brackets; provide the value under 'result[0].label' as key.
# Then get the 'text' attribute for the Lexeme object, which contains the lexeme in a human-readable form.
nlp.vocab[result[0].label].text
'pronoun+verb'

The information under the label attribute is useful for disambiguating between patterns, especially if the same Matcher object contains multiple different patterns, as we will see shortly below.

Looking at the matches above, the pattern we defined is quite restrictive, as the pronoun and the verb must follow each other.

We cannot, for example, match patterns in which the verb is preceded by auxiliary verbs.

spaCy allows increasing the flexibility of pattern rules using operators.

These operators are defined by adding the key OP to the dictionary that defines a pattern for a single Token. spaCy supports the following operators:

  • !: Negate the pattern; the pattern can occur exactly zero times.

  • ?: Make the pattern optional; the pattern may occur zero or one times.

  • +: Require the pattern to occur one or more times.

  • *: Allow the pattern to match zero or more times.

Let’s explore the use of operators by defining another pattern rule, which extends the scope of our Matcher.

To do so, we define another pattern for a Token between the pronoun and the verb. This Token must have the coarse part-of-speech tag AUX, which indicates an auxiliary verb:

{'POS': 'AUX', 'OP': '+'}

In addition, we add another key and value pair to the dictionary for this Token, which contains the key OP with the value +. This means that the Token corresponding to an auxiliary verb must occur one or more times.

We store the resulting list with nested dictionaries under the variable pronoun_aux_verb, and add the pattern to the existing Matcher object stored under the variable matcher.

# Define a list with nested dictionaries that contains the pattern to be matched
pronoun_aux_verb = [{'POS': 'PRON'}, {'POS': 'AUX', 'OP': '+'}, {'POS': 'VERB'}]

# Add the pattern to the matcher under the name 'pronoun+aux+verb'
matcher.add('pronoun+aux+verb', patterns=[pronoun_aux_verb])

# Apply the Matcher to the Doc object under 'doc'; provide the argument 'as_spans'
# and set its value to True to get Spans as output. Overwrite previous matches by
# storing the result under the variable 'results'.
results = matcher(doc, as_spans=True)

Just as above, the Matcher returns a list of spaCy Span objects.

Let’s loop over each item in the list results. We use the variable result to refer to the individual Span objects in the list, which contain our matches.

We first retrieve the Lexeme object stored under result.label, which we map to the language model’s Vocabulary under nlp.vocab.

As we learned above, this Lexeme corresponds to the name that we gave to the pattern rule, whose human-readable form can be found under the attribute text.

We then print a tabulator character to insert some space between the name of the pattern and the Span object containing the match.

# Loop over each Span object in the list 'results'
for result in results:
    
    # Print out the the name of the pattern rule, a tabulator character, and the matching Span
    print(nlp.vocab[result.label].text, '\t', result)
pronoun+verb 	 that expressed
pronoun+verb 	 It aimed
pronoun+verb 	 It formed
pronoun+verb 	 this began
pronoun+verb 	 it organizes
pronoun+aux+verb 	 that had resulted
pronoun+verb 	 that read
pronoun+verb 	 who designed
pronoun+verb 	 He wrote
pronoun+verb 	 there were
pronoun+verb 	 They promoted
pronoun+verb 	 It refers
pronoun+verb 	 which started
pronoun+verb 	 that indicate
pronoun+verb 	 that allowed
pronoun+aux+verb 	 they did have
pronoun+verb 	 they saw
pronoun+verb 	 they argued
pronoun+verb 	 they called
pronoun+verb 	 it takes
pronoun+aux+verb 	 they were working
pronoun+verb 	 that reflected
pronoun+aux+verb 	 which has been gathered
pronoun+verb 	 that strip
pronoun+aux+verb 	 who had lost
pronoun+verb 	 they called
pronoun+verb 	 that took
pronoun+aux+verb 	 themselves be informed
pronoun+aux+verb 	 that can be traced
pronoun+verb 	 who comment
pronoun+verb 	 them using
pronoun+aux+verb 	 anyone can join
pronoun+aux+verb 	 what is called
pronoun+verb 	 they belong
pronoun+verb 	 which premiered
pronoun+verb 	 himself warned
pronoun+verb 	 he said
pronoun+verb 	 they think
pronoun+aux+verb 	 they will change
pronoun+aux+verb 	 it can help
pronoun+verb 	 them gain
pronoun+verb 	 they wished
pronoun+verb 	 that saw
pronoun+verb 	 there was
pronoun+verb 	 they blamed
pronoun+aux+verb 	 It was organized
pronoun+verb 	 I support
pronoun+aux+verb 	 I saw expressed
pronoun+verb 	 Some believe
pronoun+verb 	 that followed
pronoun+verb 	 It showed
pronoun+verb 	 Some find
pronoun+verb 	 Some believe
pronoun+verb 	 there was
pronoun+aux+verb 	 It was renamed
pronoun+verb 	 which involves
pronoun+verb 	 which showed
pronoun+verb 	 who gave
pronoun+aux+verb 	 they may want
pronoun+verb 	 Some said
pronoun+verb 	 they refused
pronoun+aux+verb 	 who were arrested
pronoun+verb 	 they saw
pronoun+verb 	 who caused
pronoun+verb 	 that drew
pronoun+verb 	 there were
pronoun+verb 	 who sought
pronoun+verb 	 there were
pronoun+aux+verb 	 It was reported
pronoun+aux+verb 	 they were beginning
pronoun+aux+verb 	 they had received
pronoun+verb 	 that lasted
pronoun+verb 	 There was
pronoun+aux+verb 	 he would bring
pronoun+verb 	 This came
pronoun+verb 	 which shut
pronoun+verb 	 who made
pronoun+verb 	 that took
pronoun+verb 	 This included
pronoun+aux+verb 	 which were attended
pronoun+aux+verb 	 that were cited
pronoun+verb 	 which prohibit
pronoun+aux+verb 	 they're obligated
pronoun+aux+verb 	 which has provided
pronoun+verb 	 that hosted
pronoun+verb 	 which monitors
pronoun+aux+verb 	 which is raising
pronoun+aux+verb 	 which has developed
pronoun+verb 	 those wishing
pronoun+aux+verb 	 who were detained
pronoun+verb 	 who criticized
pronoun+verb 	 it returned
pronoun+verb 	 its proposed
pronoun+verb 	 They received
pronoun+verb 	 that advocates
pronoun+aux+verb 	 which is focused
pronoun+verb 	 there have
pronoun+verb 	 There are
pronoun+aux+verb 	 it was torn
pronoun+aux+verb 	 it has spread
pronoun+aux+verb 	 whom were left
pronoun+verb 	 it came
pronoun+verb 	 it gained
pronoun+aux+verb 	 This is attributed
pronoun+aux+verb 	 which were focused
pronoun+verb 	 He claimed
pronoun+verb 	 they presented
pronoun+verb 	 which took
pronoun+verb 	 they call
pronoun+verb 	 that started
pronoun+verb 	 which saw
pronoun+verb 	 all set
pronoun+aux+verb 	 it was reported
pronoun+verb 	 there were
pronoun+verb 	 It consists
pronoun+verb 	 there were
pronoun+verb 	 there were
pronoun+verb 	 which started
pronoun+verb 	 there was
pronoun+aux+verb 	 which was evicted
pronoun+verb 	 which featured
pronoun+verb 	 all finding
pronoun+verb 	 What started
pronoun+aux+verb 	 who were occupying
pronoun+aux+verb 	 that could be used
pronoun+verb 	 there was
pronoun+aux+verb 	 It was expected
pronoun+aux+verb 	 it was disbanded
pronoun+aux+verb 	 it was fenced
pronoun+verb 	 There was
pronoun+verb 	 Some said
pronoun+verb 	 they began
pronoun+verb 	 they perceived
pronoun+verb 	 that threaten
pronoun+verb 	 they say
pronoun+verb 	 We agree
pronoun+verb 	 we see
pronoun+verb 	 There's
pronoun+aux+verb 	 I can understand
pronoun+aux+verb 	 it will grow
pronoun+aux+verb 	 it will bring
pronoun+verb 	 There are
pronoun+verb 	 who say
pronoun+aux+verb 	 we can build
pronoun+verb 	 they do
pronoun+aux+verb 	 they're penalized
pronoun+verb 	 they reflect
pronoun+verb 	 He mentioned
pronoun+verb 	 We regard
pronoun+verb 	 who participated
pronoun+aux+verb 	 they were removed
pronoun+verb 	 he wrote
pronoun+verb 	 we have
pronoun+aux+verb 	 they have been protesting
pronoun+aux+verb 	 they will have made
pronoun+verb 	 who dislike
pronoun+verb 	 that burdens
pronoun+verb 	 they employ
pronoun+verb 	 they have
pronoun+aux+verb 	 it has cleared
pronoun+verb 	 there were
pronoun+aux+verb 	 which would overturn
pronoun+aux+verb 	 it would have
pronoun+aux+verb 	 what became known
pronoun+aux+verb 	 that were scheduled
pronoun+verb 	 that differs
pronoun+verb 	 that follow
pronoun+verb 	 there is
pronoun+verb 	 that abstract
pronoun+verb 	 it stall
pronoun+verb 	 who emerged
pronoun+verb 	 It pushes
pronoun+verb 	 who called
pronoun+aux+verb 	 whom have observed
pronoun+aux+verb 	 who are running
pronoun+verb 	 that deals
pronoun+aux+verb 	 you're going
pronoun+verb 	 that believe

The output shows that the pattern we added to the Matcher matches patterns that contain one (e.g. “we can build”) or more (e.g. “they have been protesting”) auxiliaries!

Matching morphological features

As introduced in Part II, spaCy can also perform morphological analysis, whose results are stored under the attribute morph of a Token object.

The morph attribute contains a string object, in which each morphological feature is separated by a vertical bar |, as illustrated below.

We 	 Case=Nom|Number=Plur|Person=1|PronType=Prs

As you can see, particular types of morphological features, e.g. Case, and their type, e.g. Nom (for the nominative case) are separated by equal signs =.

Let’s begin exploring how we can define pattern rules that match morphological features.

To get started, we create a new Matcher object named morph_matcher.

# Create a Matcher and provide model vocabulary; assign result under the variable 'morph_matcher'
morph_matcher = Matcher(vocab=nlp.vocab)

We then define a new pattern with rules for two Tokens:

  1. Tokens that have a fine-grained part-of-speech tag NNP (proper noun), which can occur one or more times (operator: +).

{'TAG': 'NNP', 'OP': '+'}
  1. Tokens that have a coarse part-of-speech tag VERB and have all the following morphological features (MORPH): Number=Sing|Person=Three|Tense=Pres|VerbForm=Fin.

{'POS': 'VERB', 'MORPH': 'Number=Sing|Person=Three|Tense=Pres|VerbForm=Fin'}

We define the pattern using two dictionaries in a list, which we assign under the variable propn_3rd_finite.

# Define a list with nested dictionaries that contains the pattern to be matched
propn_3rd_finite = [{'TAG': 'NNP', 'OP': '+'},
                    {'POS': 'VERB', 'MORPH': 'Number=Sing|Person=Three|Tense=Pres|VerbForm=Fin'}]

We then add the pattern to the newly-created Matcher stored under the variable morph_matcher using the add() method.

We also provide the value LONGEST to the optional argument greedy for the add() method.

The greedy argument filters the matches for Tokens that include operators such as + that search greedily for more than one match.

By setting the value to LONGEST, spaCy returns the longest sequence of matches instead of returning a match every time it finds one. Put differently, spaCy will collect all the matching Tokens before returning them.

# Add the pattern to the matcher under the name 'sing_3rd_pres_fin'
morph_matcher.add('sing_3rd_pres_fin', patterns=[propn_3rd_finite], greedy='LONGEST')

We then apply the Matcher to the data stored under the variable doc.

# Apply the Matcher to the Doc object under 'doc'; provide the argument 'as_spans'
# and set its value to True to get Spans as output. Overwrite previous matches by
# storing the result under the variable 'results'.
morph_results = morph_matcher(doc, as_spans=True)

# Loop over each Span object in the list 'morph_results'
for result in morph_results:

    # Print result
    print(result)

As you can see, the matches are relatively few in number, because we defined that the verb should have quite specific morphological features.

The question is, then, how can we match just some morphological features?

To loosen the criteria for morphological features by focusing on tense only, we need to use a dictionary with the key MORPH, but instead of a string object, we provide a dictionary as its value:

For this dictionary, we use the string IS_SUPERSET as the key. IS_SUPERSET is one of the attributes defined in the spaCy pattern format, e.g.

{'MORPH': {'IS_SUPERSET': [...]}}

Before proceeding any further, let’s unpack the logic behind IS_SUPERSET a bit.

We can think of morphological features associated with a given Token as a set. To exemplify, a set could consist of the following four items:

Number=Sing, Person=Three, Tense=Pres, VerbForm=Fin

If we would have another set with just one item, Tense=Pres, we could describe the relationship between the two sets by stating that the first set (with four items) is a superset of the second set (with one item).

In other words, the larger (super)set contains the smaller (sub)set.

This is also how matching using IS_SUPERSET works: spaCy retrieves the morphological features for each Token, and examines whether these features are a superset of the features defined in the search pattern.

The morphological features to be searched for are provided as a list of Python strings.

These strings, in turn, define particular morphological features, e.g. Tense=Past, as defined in the Universal Dependencies schema for describing morphology, which was introduced in the previous section.

This list is then used as the value for the key IS_SUPERSET.

Let’s now proceed to search for verbs in the past tense and add them to the Matcher object under morph_matcher.

# Define a list with nested dictionaries that contains the pattern to be matched
past_tense = [{'TAG': 'NNP', 'OP': '+'},
              {'POS': 'VERB', 'MORPH': {'IS_SUPERSET': ['Tense=Past']}}]

# Add the pattern to the matcher under the name 'past_tense'
morph_matcher.add('past_tense', patterns=[past_tense], greedy='LONGEST')

# Apply the Matcher to the Doc object under 'doc'; provide the argument 'as_spans'
# and set its value to True to get Spans as output. Overwrite previous matches by
# storing the result under the variable 'results'.
morph_results = morph_matcher(doc, as_spans=True)

Let’s loop over the results and print out the name of the pattern, the Span object containing the match, and the morphological features of the final Token in the match, which corresponds to the verb.

# Loop over each Span object in the list 'results'
for result in morph_results:
    
    # Print out the the name of the pattern rule, a tabulator character, and the matching Span.
    # Finally, print another tabulator character, followed by the morphological features of the
    # last Token in the match (a verb).
    print(nlp.vocab[result.label].text, '\t', result, '\t', result[-1].morph)
past_tense 	 Community Environmental Legal Defense Fund released 	 Tense=Past|VerbForm=Fin
past_tense 	 Oakland Police Chief Howard Jordan expressed 	 Tense=Past|VerbForm=Fin
past_tense 	 U.S. Vice President Al Gore called 	 Tense=Past|VerbForm=Fin
past_tense 	 Los Angeles City Council became 	 Tense=Past|VerbForm=Fin
past_tense 	 Judge Jed S. Rakoff sided 	 Tense=Past|VerbForm=Fin
past_tense 	 Finance Minister Jim Flaherty expressed 	 Tense=Past|VerbForm=Fin
past_tense 	 Prime Minister Manmohan Singh described 	 Tense=Past|VerbForm=Fin
past_tense 	 Supreme Leader Ayatollah Khamenei voiced 	 Tense=Past|VerbForm=Fin
past_tense 	 Prime Minister Gordon Brown said 	 Tense=Past|VerbForm=Fin
past_tense 	 Anti-Defamation League stated 	 Tense=Past|VerbForm=Fin
past_tense 	 Occupy Wall Street endorsed 	 Tense=Past|VerbForm=Fin
past_tense 	 New York Times reported 	 Tense=Past|VerbForm=Fin
past_tense 	 Occupy Wall Street said 	 Tense=Past|VerbForm=Fin
past_tense 	 Lieutenant John Pike used 	 Tense=Past|VerbForm=Fin
past_tense 	 Occupy Wall Street attempted 	 Tense=Past|VerbForm=Fin
past_tense 	 Mayor Charlie Hales ordered 	 Tense=Past|VerbForm=Fin
past_tense 	 Pietro al Laterano received 	 Tense=Past|VerbForm=Fin
past_tense 	 Taksim Gezi Park developed 	 Tense=Past|VerbForm=Fin
past_tense 	 International Press Institute commented 	 Tense=Past|VerbForm=Fin
past_tense 	 President Dilma Rousseff said 	 Tense=Past|VerbForm=Fin
past_tense 	 Edinburgh City Council set 	 Tense=Past|VerbForm=Fin
past_tense 	 President Barack Obama spoke 	 Tense=Past|VerbForm=Fin
past_tense 	 New York City sent 	 Tense=Past|VerbForm=Fin
past_tense 	 President Hugo Chávez condemned 	 Tense=Past|VerbForm=Fin
past_tense 	 American Dialect Society voted 	 Tense=Past|VerbForm=Fin
past_tense 	 Manfred Steger called 	 Tense=Past|VerbForm=Fin
past_tense 	 Cornel West described 	 Tense=Past|VerbForm=Fin
past_tense 	 Huffington Post noted 	 Tense=Past|VerbForm=Fin
past_tense 	 Kalle Lasn registered 	 Tense=Past|VerbForm=Fin
past_tense 	 Democracy Village set 	 Tense=Past|VerbForm=Fin
past_tense 	 Naomi Wolf argued 	 Tense=Past|VerbForm=Fin
past_tense 	 Judith Butler criticized 	 Tense=Past|VerbForm=Fin
past_tense 	 People Link offered 	 Tense=Past|VerbForm=Fin
past_tense 	 Manuel Castells congratulated 	 Tense=Past|VerbForm=Fin
past_tense 	 Naomi Klein congratulated 	 Tense=Past|VerbForm=Fin
past_tense 	 USA Today said 	 Tense=Past|VerbForm=Fin
past_tense 	 Anthony Barnett said 	 Tense=Past|VerbForm=Fin
past_tense 	 Kanye West justified 	 Tense=Past|VerbForm=Fin
past_tense 	 Michael Moore tweeted 	 Tense=Past|VerbForm=Fin
past_tense 	 WikiLeaks Central began 	 Tense=Past|VerbForm=Fin
past_tense 	 Alexa O'Brien modeled 	 Tense=Past|VerbForm=Fin
past_tense 	 Richard Lambert suggested 	 Tense=Past|VerbForm=Fin
past_tense 	 Shannon Bond found 	 Tense=Past|VerbForm=Fin
past_tense 	 Washington Post reported 	 Tense=Past|VerbForm=Fin
past_tense 	 Occupy Nigeria began 	 Tense=Past|VerbForm=Fin
past_tense 	 January Jonathan responded 	 Tense=Past|VerbForm=Fin
past_tense 	 Hurricane Sandy hit 	 Tense=Past|VerbForm=Fin
past_tense 	 Bernie Sanders protested 	 Tense=Past|VerbForm=Fin
past_tense 	 Occupy Movement organized 	 Tense=Past|VerbForm=Fin
past_tense 	 Occupy Kalamazoo began 	 Tense=Past|VerbForm=Fin
past_tense 	 Occupy Sydney had 	 Tense=Past|VerbForm=Fin
past_tense 	 Pirate Party participated 	 Tense=Past|VerbForm=Fin
past_tense 	 United Nations controlled 	 Aspect=Perf|Tense=Past|VerbForm=Part
past_tense 	 Occupy Berlin established 	 Tense=Past|VerbForm=Fin
past_tense 	 High Court ruled 	 Tense=Past|VerbForm=Fin
past_tense 	 Irish Times described 	 Tense=Past|VerbForm=Fin
past_tense 	 Occupy Seoul contained 	 Tense=Past|VerbForm=Fin
past_tense 	 South Korea overcame 	 Tense=Past|VerbForm=Fin
past_tense 	 M Movement drew 	 Tense=Past|VerbForm=Fin
past_tense 	 Lancaster Police arrested 	 Tense=Past|VerbForm=Fin
past_tense 	 Occupy Belfast initiated 	 Tense=Past|VerbForm=Fin
past_tense 	 Occupy Belfast took 	 Tense=Past|VerbForm=Fin
past_tense 	 Occupy Coleraine took 	 Tense=Past|VerbForm=Fin
past_tense 	 Occupy Glasgow set 	 Aspect=Perf|Tense=Past|VerbForm=Part
past_tense 	 Occupy Cardiff set 	 Tense=Past|VerbForm=Fin
past_tense 	 Francis Fukuyama argued 	 Tense=Past|VerbForm=Fin
past_tense 	 American Progress suggested 	 Tense=Past|VerbForm=Fin
past_tense 	 Richard Branson said 	 Tense=Past|VerbForm=Fin
past_tense 	 Jesse Jackson said 	 Tense=Past|VerbForm=Fin
past_tense 	 Daily Telegraph reported 	 Tense=Past|VerbForm=Fin
past_tense 	 Financial Times argued 	 Tense=Past|VerbForm=Fin
past_tense 	 Paul Mason said 	 Tense=Past|VerbForm=Fin
past_tense 	 Atlantic Magazine declared 	 Tense=Past|VerbForm=Fin
past_tense 	 England stated 	 Tense=Past|VerbForm=Fin
past_tense 	 California occupied 	 Tense=Past|VerbForm=Fin
past_tense 	 Spain marked 	 Tense=Past|VerbForm=Fin
past_tense 	 Anonymous encouraged 	 Tense=Past|VerbForm=Fin
past_tense 	 U.S. saw 	 Tense=Past|VerbForm=Fin
past_tense 	 Wolf argued 	 Tense=Past|VerbForm=Fin
past_tense 	 Indymedia helped 	 Tense=Past|VerbForm=Fin
past_tense 	 Occupy related 	 Aspect=Perf|Tense=Past|VerbForm=Part
past_tense 	 WikiLeaks endorsed 	 Tense=Past|VerbForm=Fin
past_tense 	 October included 	 Tense=Past|VerbForm=Fin
past_tense 	 Gapper said 	 Tense=Past|VerbForm=Fin
past_tense 	 Occupy protested 	 Tense=Past|VerbForm=Fin
past_tense 	 Feds ordered 	 Tense=Past|VerbForm=Fin
past_tense 	 HSBC filed 	 Tense=Past|VerbForm=Fin
past_tense 	 Rome masked 	 Tense=Past|VerbForm=Fin
past_tense 	 Occupy began 	 Tense=Past|VerbForm=Fin
past_tense 	 Norway began 	 Tense=Past|VerbForm=Fin
past_tense 	 NEET troubled 	 Tense=Past|VerbForm=Fin
past_tense 	 June included 	 Tense=Past|VerbForm=Fin
past_tense 	 Vancouver added 	 Tense=Past|VerbForm=Fin
past_tense 	 Conan launched 	 Tense=Past|VerbForm=Fin
past_tense 	 Occupy influenced 	 Tense=Past|VerbForm=Fin
past_tense 	 FBI offered 	 Tense=Past|VerbForm=Fin
past_tense 	 FBI used 	 Tense=Past|VerbForm=Fin
past_tense 	 FBI withheld 	 Tense=Past|VerbForm=Fin
past_tense 	 FBI refused 	 Tense=Past|VerbForm=Fin
past_tense 	 Shapiro filed 	 Tense=Past|VerbForm=Fin

As you can see, the past_tense pattern can match objects based on a single morphological feature, although most matches share another morphological feature, namely the finite form.

Matching syntactic dependencies

If you want to match patterns based on syntactic dependencies, you must use the DependencyMatcher class in spaCy.

As we learned in Part II, syntactic dependencies describe the relations that hold between Token objects.

To get started, let’s import the DependencyMatcher class from the matcher submodule.

As you can see, the DependencyMatcher is initialised just as like the Matcher above.

# Import the DependencyMatcher class
from spacy.matcher import DependencyMatcher

# Create a DependencyMatcher and provide model vocabulary; 
# assign result under the variable 'dep_matcher'
dep_matcher = DependencyMatcher(vocab=nlp.vocab)

This provides us with a DependencyMatcher stored under the variable dep_matcher, which is now ready for storing dependency patterns.

When developing pattern rules for matching syntactic dependencies, the first step is to determine an “anchor” around which the pattern is built.

Visualising the syntactic dependencies, as instructed in Part II, can help formulate patterns.

Let’s import the displacy submodule to draw the syntactic dependencies for a sentence in the Doc object stored under the variable doc.

# Import the displacy submodule from spaCy
from spacy import displacy

# Cast the sentences contained in the Doc object into a list; take the sentence
# at index 420. Set the 'style' attribute to 'dep' to draw syntactic dependencies. 
displacy.render(list(doc.sents)[420], style='dep')
On ADP 17 NUM September PROPN 2012, NUM protesters NOUN returned VERB to ADP Zuccotti PROPN Park PROPN to PART mark VERB the DET one- NUM year NOUN anniversary NOUN of ADP the DET beginning NOUN of ADP the DET occupation. NOUN prep nummod pobj nummod nsubj prep compound pobj aux advcl det nummod compound dobj prep det pobj prep det pobj

As introduced in Part III, syntactic dependencies are visualised using arcs that lead from the head Token to the dependent Token. The label of the arc gives the syntactic dependency.

Let’s define a pattern that searches for verbs and their nominal subjects (nsubj).

Just as using the Matcher class, the pattern rules for the DependencyMatcher are defined using a list of dictionaries.

The first dictionary in the list defines an “anchor” pattern and its attributes.

You can think of the pattern rule as a chain that proceeds from left to right, and the first pattern on the left acts as an anchor for the subsequent patterns on its right-hand side.

Hence we define the following pattern for the anchor:

{'RIGHT_ID': 'verb', 'RIGHT_ATTRS': {'POS': 'VERB'}}

We use the required key RIGHT_ID to provide a name for this pattern, which can be then used to refer to this pattern by subsequent patterns on its right-hand side.

In other words, when you see the key RIGHT_ID, think of a name for the current pattern.

We then create a dictionary under the key RIGHT_ATTRS that holds the linguistic features of the anchor. In this case, we determine that the anchor should have VERB as its part-of-speech tag.

Next, we determine a pattern for the next “link” in the chain to the right of the anchor:

{'LEFT_ID': 'verb', 'REL_OP': '>', 'RIGHT_ID': 'subject', 'RIGHT_ATTRS': {'DEP': 'nsubj'}}

We start by providing the key LEFT_ID, whose value is a string object that refers to the name of a pattern on the left-hand side of the current pattern. This is the name that we gave to the anchor using the key RIGHT_ID.

Next, we use the key REL_OP to define a relation operator, which determines the relationship between this pattern and that referred to using LEFT_ID.

The relation operator > defines that the pattern under LEFT_ID – the anchor – should be the head of the current pattern.

Next, we name the current pattern using the key RIGHT_ID, which enables referring to this pattern on the right-hand side, if necessary. We give this pattern the name subject.

We then use the RIGHT_ATTRS to determine the attributes for the current pattern. We define that the syntactic relation that holds between this pattern and that on the left should be nsubj or nominal subject.

# Define a list with nested dictionaries that contains the pattern to be matched
dep_pattern = [{'RIGHT_ID': 'verb', 'RIGHT_ATTRS': {'POS': 'VERB'}},
               {'LEFT_ID': 'verb', 'REL_OP': '>', 'RIGHT_ID': 'subject', 'RIGHT_ATTRS': {'DEP': 'nsubj'}}
              ]

We then compile these two dictionaries into a list, add the pattern to the DependencyMatcher under dep_matcher and search the Doc object doc for matches.

We store the resulting matches under the variable dep_matches and call this variable to examine the output.

# Add the pattern to the matcher under the name 'nsubj_verb'
dep_matcher.add('nsubj_verb', patterns=[dep_pattern])

# Apply the DependencyMatcher to the Doc object under 'doc'; Store the result 
# under the variable 'dep_matches'.
dep_matches = dep_matcher(doc)

# Call the variable to examine the output
dep_matches
[(5549296207297668001, [15, 14]),
 (5549296207297668001, [37, 36]),
 (5549296207297668001, [53, 52]),
 (5549296207297668001, [62, 60]),
 (5549296207297668001, [70, 69]),
 (5549296207297668001, [81, 73]),
 (5549296207297668001, [89, 87]),
 (5549296207297668001, [100, 99]),
 (5549296207297668001, [106, 105]),
 (5549296207297668001, [133, 122]),
 (5549296207297668001, [146, 144]),
 (5549296207297668001, [172, 171]),
 (5549296207297668001, [188, 184]),
 (5549296207297668001, [207, 206]),
 (5549296207297668001, [212, 211]),
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 (5549296207297668001, [329, 328]),
 (5549296207297668001, [349, 348]),
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 (5549296207297668001, [501, 492]),
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 (5549296207297668001, [588, 587]),
 (5549296207297668001, [596, 585]),
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 (5549296207297668001, [633, 632]),
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 (5549296207297668001, [1048, 1047]),
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 (5549296207297668001, [1869, 1864]),
 (5549296207297668001, [1879, 1878]),
 (5549296207297668001, [1906, 1905]),
 (5549296207297668001, [1935, 1923]),
 (5549296207297668001, [1939, 1937]),
 (5549296207297668001, [1948, 1947]),
 (5549296207297668001, [1981, 1979]),
 (5549296207297668001, [2001, 2000]),
 (5549296207297668001, [2051, 2045]),
 (5549296207297668001, [2081, 2080]),
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Unlike the Matcher, the DependencyMatcher cannot return the matches as Span objects, because the matches do not necessarily form a continuous sequence of Tokens needed for a Span object.

Thus the DependencyMatcher returns a list of tuples.

Each tuple contains two items:

  1. A Lexeme object that gives the name of the pattern

  2. A list of indices for Tokens that match the search pattern in the Doc object

# Loop over each tuple in the list 'dep_matches'
for match in dep_matches:
    
    # Take the first item in the tuple at [0] and assign it under
    # the variable 'pattern_name'. This item is a spaCy Lexeme object.
    pattern_name = match[0]
    
    # Take the second item in the tuple at [1] and assign it under
    # the variable 'matches'. This is a list of indices referring to the
    # Doc object under 'doc' that we just matched.
    matches = match[1]
    
    # Let's unpack the matches list into variables for clarity
    verb, subject = matches[0], matches[1]
    
    # Print the matches by first fetching the name of the pattern from the 
    # Vocabulary object. Next, use the 'subject' and 'verb' variables to 
    # index the Doc object. This gives us the actual Tokens matched. Use a
    # tabulator ('\t') and some stops ('...') to separate the output.
    print(nlp.vocab[pattern_name].text, '\t', doc[subject], '...', doc[verb])
nsubj_verb 	 that ... expressed
nsubj_verb 	 It ... aimed
nsubj_verb 	 movement ... had
nsubj_verb 	 groups ... had
nsubj_verb 	 concerns ... included
nsubj_verb 	 corporations ... control
nsubj_verb 	 that ... benefited
nsubj_verb 	 It ... formed
nsubj_verb 	 Steger ... called
nsubj_verb 	 Occupy ... began
nsubj_verb 	 protests ... taken
nsubj_verb 	 movement ... became
nsubj_verb 	 protests ... started
nsubj_verb 	 repression ... remained
nsubj_verb 	 this ... began
nsubj_verb 	 police ... attempted
nsubj_verb 	 authorities ... cleared
nsubj_verb 	 movement ... uses
nsubj_verb 	 it ... organizes
nsubj_verb 	 West ... described
nsubj_verb 	 Director ... stated
nsubj_verb 	 students ... occupied
nsubj_verb 	 that ... resulted
nsubj_verb 	 slogan ... emerged
nsubj_verb 	 Post ... noted
nsubj_verb 	 that ... read
nsubj_verb 	 who ... designed
nsubj_verb 	 White ... traveled
nsubj_verb 	 He ... wrote
nsubj_verb 	 movement ... began
nsubj_verb 	 camping ... marked
nsubj_verb 	 leader ... called
nsubj_verb 	 Foundation ... proposed
nsubj_verb 	 Lasn ... registered
nsubj_verb 	 we ... floated
nsubj_verb 	 it ... snowballed
nsubj_verb 	 Village ... set
nsubj_verb 	 protest ... received
nsubj_verb 	 group ... encouraged
nsubj_verb 	 They ... promoted
nsubj_verb 	 It ... refers
nsubj_verb 	 percent ... tripled
nsubj_verb 	 incomes ... grew
nsubj_verb 	 % ... saw
nsubj_verb 	 income ... decreased
nsubj_verb 	 that ... increased
nsubj_verb 	 taxation ... became
nsubj_verb 	 earners ... saw
nsubj_verb 	 income ... increase
nsubj_verb 	 % ... owned
nsubj_verb 	 % ... owned
nsubj_verb 	 % ... owned
nsubj_verb 	 % ... owning
nsubj_verb 	 which ... started
nsubj_verb 	 share ... grew
nsubj_verb 	 that ... grew
nsubj_verb 	 Recession ... caused
nsubj_verb 	 income ... grew
nsubj_verb 	 % ... went
nsubj_verb 	 who ... had
nsubj_verb 	 that ... indicate
nsubj_verb 	 Lasn ... said
nsubj_verb 	 that ... allowed
nsubj_verb 	 movement ... grow
nsubj_verb 	 Adbusters ... trying
nsubj_verb 	 Wolf ... argued
nsubj_verb 	 Wolf ... argued
nsubj_verb 	 they ... have
nsubj_verb 	 they ... saw
nsubj_verb 	 magazine ... stated
nsubj_verb 	 protesters ... wanted
nsubj_verb 	 commentators ... criticized
nsubj_verb 	 movement ... defined
nsubj_verb 	 they ... argued
nsubj_verb 	 movement ... seeks
nsubj_verb 	 contingent ... released
nsubj_verb 	 they ... called
nsubj_verb 	 it ... takes
nsubj_verb 	 Occupy ... said
nsubj_verb 	 they ... working
nsubj_verb 	 that ... reflected
nsubj_verb 	 Activists ... used
nsubj_verb 	 Indymedia ... helped
nsubj_verb 	 provider ... offered
nsubj_verb 	 movement ... went
nsubj_verb 	 Fund ... released
nsubj_verb 	 that ... strip
nsubj_verb 	 Homes ... embarked
nsubj_verb 	 who ... lost
nsubj_verb 	 they ... called
nsubj_verb 	 that ... took
nsubj_verb 	 group ... planned
nsubj_verb 	 Much ... occurs
nsubj_verb 	 This ... features
nsubj_verb 	 who ... comment
nsubj_verb 	 anyone ... join
nsubj_verb 	 Street ... uses
nsubj_verb 	 they ... belong
nsubj_verb 	 women ... get
nsubj_verb 	 males ... wait
nsubj_verb 	 turn ... speak
nsubj_verb 	 movement ... began
nsubj_verb 	 which ... premiered
nsubj_verb 	 Sharp ... warned
nsubj_verb 	 movement ... employing
nsubj_verb 	 he ... said
nsubj_verb 	 protesters ... have
nsubj_verb 	 they ... achieve
nsubj_verb 	 they ... think
nsubj_verb 	 they ... change
nsubj_verb 	 Protest ... accomplishes
nsubj_verb 	 Castells ... congratulated
nsubj_verb 	 Castells ... said
nsubj_verb 	 it ... help
nsubj_verb 	 them ... gain
nsubj_verb 	 they ... make
nsubj_verb 	 Group ... endorsed
nsubj_verb 	 occupiers ... upheld
nsubj_verb 	 journalists ... saying
nsubj_verb 	 branch ... accept
nsubj_verb 	 who ... signed
nsubj_verb 	 Klein ... congratulated
nsubj_verb 	 sources ... began
nsubj_verb 	 camps ... responded
nsubj_verb 	 occupiers ... sign
nsubj_verb 	 they ... wished
nsubj_verb 	 Hampton ... said
nsubj_verb 	 Barnett ... said
nsubj_verb 	 nonviolence ... remained
nsubj_verb 	 that ... saw
nsubj_verb 	 protestors ... said
nsubj_verb 	 police ... initiated
nsubj_verb 	 others ... said
nsubj_verb 	 they ... blamed
nsubj_verb 	 who ... take
nsubj_verb 	 protester ... stated
nsubj_verb 	 I ... support
nsubj_verb 	 who ... have
nsubj_verb 	 I ... support
nsubj_verb 	 I ... expressed
nsubj_verb 	 movement ... relied
nsubj_verb 	 accounts ... became
nsubj_verb 	 Some ... believe
nsubj_verb 	 that ... followed
nsubj_verb 	 interests ... changed
nsubj_verb 	 It ... showed
nsubj_verb 	 ratio ... dropping
nsubj_verb 	 Some ... find
nsubj_verb 	 celebrities ... made
nsubj_verb 	 West ... justified
nsubj_verb 	 celebrities ... tweeted
nsubj_verb 	 Moore ... tweeted
nsubj_verb 	 Many ... hold
nsubj_verb 	 
success ... led
nsubj_verb 	 Some ... believe
nsubj_verb 	 people ... used
nsubj_verb 	 WikiLeaks ... endorsed
nsubj_verb 	 Central ... began
nsubj_verb 	 editor ... modeled
nsubj_verb 	 protests ... began
nsubj_verb 	 activists ... repeated
nsubj_verb 	 list ... included
nsubj_verb 	 protesters ... gathered
nsubj_verb 	 people ... stayed
nsubj_verb 	 protesters ... started
nsubj_verb 	 officers ... used
nsubj_verb 	 which ... involves
nsubj_verb 	 which ... showed
nsubj_verb 	 attention ... resulted
nsubj_verb 	 Haberman ... said
nsubj_verb 	 protesters ... choose
nsubj_verb 	 who ... gave
nsubj_verb 	 they ... want
nsubj_verb 	 protesters ... set
nsubj_verb 	 Times ... reported
nsubj_verb 	 Some ... said
nsubj_verb 	 police ... tricked
nsubj_verb 	 Myerson ... said
nsubj_verb 	 cops ... watched
nsubj_verb 	 spokesman ... said
nsubj_verb 	 they ... refused
nsubj_verb 	 group ... filed
nsubj_verb 	 officers ... violated
nsubj_verb 	 judge ... ruled
nsubj_verb 	 protesters ... received
nsubj_verb 	 evidence ... showed
nsubj_verb 	 police ... warning
nsubj_verb 	 Rakoff ... sided
nsubj_verb 	 officer ... known
nsubj_verb 	 horn ... communicate
nsubj_verb 	 demonstration ... swelled
nsubj_verb 	 marchers ... joining
nsubj_verb 	 protests ... continued
nsubj_verb 	 Thousands ... joined
nsubj_verb 	 protesters ... marching
nsubj_verb 	 scuffles ... erupted
nsubj_verb 	 protesters ... tried
nsubj_verb 	 Police ... responded
nsubj_verb 	 protesters ... organized
nsubj_verb 	 they ... saw
nsubj_verb 	 One ... said
nsubj_verb 	 Government ... made
nsubj_verb 	 who ... caused
nsubj_verb 	 crisis ... get
nsubj_verb 	 people ... pay
nsubj_verb 	 thousands ... staging
nsubj_verb 	 people ... protested
nsubj_verb 	 protesters ... carried
nsubj_verb 	 We ... bail
nsubj_verb 	 that ... drew
nsubj_verb 	 protest ... turned
nsubj_verb 	 Thousands ... gathered
nsubj_verb 	 police ... cleared
nsubj_verb 	 Jordan ... expressed
nsubj_verb 	 police ... suffered
nsubj_verb 	 who ... sought
nsubj_verb 	 Olsen ... suffered
nsubj_verb 	 protesters ... shut
nsubj_verb 	 Police ... estimated
nsubj_verb 	 4,500 ... marched
nsubj_verb 	 protesters ... held
nsubj_verb 	 people ... took
nsubj_verb 	 police ... removed
nsubj_verb 	 authorities ... stepped
nsubj_verb 	 Lambert ... suggested
nsubj_verb 	 it ... disband
nsubj_verb 	 Gapper ... offered
nsubj_verb 	 Gapper ... said
nsubj_verb 	 they ... beginning
nsubj_verb 	 Pike ... used
nsubj_verb 	 incident ... drew
nsubj_verb 	 Katehi ... resign
nsubj_verb 	 occupiers ... checked
nsubj_verb 	 they ... received
nsubj_verb 	 occupiers ... begun
nsubj_verb 	 Bond ... found
nsubj_verb 	 issues ... included
nsubj_verb 	 Homes ... joined
nsubj_verb 	 activists ... planted
nsubj_verb 	 that ... lasted
nsubj_verb 	 Post ... reported
nsubj_verb 	 which ... disbanded
nsubj_verb 	 some ... facing
nsubj_verb 	 Nigeria ... began
nsubj_verb 	 most ... took
nsubj_verb 	 strikes ... shutting
nsubj_verb 	 Jonathan ... responded
nsubj_verb 	 he ... bring
nsubj_verb 	 2012 ... seen
nsubj_verb 	 universities ... begun
nsubj_verb 	 course ... includes
nsubj_verb 	 students ... join
nsubj_verb 	 teams ... planning
nsubj_verb 	 LLC ... reached
nsubj_verb 	 agreement ... resolved
nsubj_verb 	 workers ... work
nsubj_verb 	 This ... came
nsubj_verb 	 which ... shut
nsubj_verb 	 goals ... included
nsubj_verb 	 poll ... found
nsubj_verb 	 supporters ... outweighed
nsubj_verb 	 Occupy ... protested
nsubj_verb 	 Street ... attempted
nsubj_verb 	 who ... made
nsubj_verb 	 movement ... marked
nsubj_verb 	 that ... took
nsubj_verb 	 This ... included
nsubj_verb 	 members ... gathered
nsubj_verb 	 movement ... celebrated
nsubj_verb 	 activists ... set
nsubj_verb 	 activists ... claimed
nsubj_verb 	 which ... prohibit
nsubj_verb 	 occupiers ... claim
nsubj_verb 	 beings ... need
nsubj_verb 	 people ... protect
nsubj_verb 	 activists ... said
nsubj_verb 	 vigil ... continue
nsubj_verb 	 Hales ... ordered
nsubj_verb 	 movement ... transformed
nsubj_verb 	 campaigns ... emerged
nsubj_verb 	 campaigns ... include
nsubj_verb 	 which ... provided
nsubj_verb 	 Sandy ... hit
nsubj_verb 	 that ... hosted
nsubj_verb 	 which ... monitors
nsubj_verb 	 which ... raising
nsubj_verb 	 individual ... re
nsubj_verb 	 which ... developed
nsubj_verb 	 program ... worked
nsubj_verb 	 hundreds ... protested
nsubj_verb 	 supporters ... protesting
nsubj_verb 	 Sanders ... received
nsubj_verb 	 protestors ... claiming
nsubj_verb 	 networks ... blacked
nsubj_verb 	 spirit ... lives
nsubj_verb 	 anarchism ... began
nsubj_verb 	 Cafe ... continues
nsubj_verb 	 Movement ... organized
nsubj_verb 	 groups ... emerged
nsubj_verb 	 group ... swarmed
nsubj_verb 	 it ... shutdown
nsubj_verb 	 hundreds ... took
nsubj_verb 	 blockade ... caused
nsubj_verb 	 building ... shutdown
nsubj_verb 	 staff ... citing
nsubj_verb 	 Feds ... ordered
nsubj_verb 	 officers ... moved
nsubj_verb 	 Kalamazoo ... began
nsubj_verb 	 efforts ... received
nsubj_verb 	 who ... criticized
nsubj_verb 	 protesters ... faced
nsubj_verb 	 demonstrations ... continuing
nsubj_verb 	 leader ... named
nsubj_verb 	 demonstrations ... took
nsubj_verb 	 protesters ... defied
nsubj_verb 	 Occupiers ... returned
nsubj_verb 	 Sydney ... had
nsubj_verb 	 it ... returned
nsubj_verb 	 demonstration ... took
nsubj_verb 	 movement ... had
nsubj_verb 	 people ... attended
nsubj_verb 	 Three ... took
nsubj_verb 	 one ... took
nsubj_verb 	 protests ... included
nsubj_verb 	 protesters ... say
nsubj_verb 	 Ghent ... began
nsubj_verb 	 They ... received
nsubj_verb 	 that ... advocates
nsubj_verb 	 protests ... taken
nsubj_verb 	 people ... gathered
nsubj_verb 	 150 ... stayed
nsubj_verb 	 people ... marched
nsubj_verb 	 100 ... continued
nsubj_verb 	 1,000 ... gathered
nsubj_verb 	 people ... occupied
nsubj_verb 	 people ... occupied
nsubj_verb 	 group ... occupied
nsubj_verb 	 protestors ... began
nsubj_verb 	 Party ... participated
nsubj_verb 	 Police ... dissolved
nsubj_verb 	 protesters ... started
nsubj_verb 	 movement ... used
nsubj_verb 	 protesters ... showed
nsubj_verb 	 camp ... lived
nsubj_verb 	 movement ... shifted
nsubj_verb 	 protesters ... started
nsubj_verb 	 relations ... varied
nsubj_verb 	 police ... joined
nsubj_verb 	 people ... joined
nsubj_verb 	 protests ... begun
nsubj_verb 	 students ... began
nsubj_verb 	 it ... spread
nsubj_verb 	 movement ... began
nsubj_verb 	 Occupy ... took
nsubj_verb 	 Berlin ... established
nsubj_verb 	 protests ... took
nsubj_verb 	 Police ... reported
nsubj_verb 	 people ... protested
nsubj_verb 	 people ... took
nsubj_verb 	 organisers ... claimed
nsubj_verb 	 HSBC ... filed
nsubj_verb 	 Court ... ruled
nsubj_verb 	 protesters ... leave
nsubj_verb 	 people ... gathered
nsubj_verb 	 protests ... occurred
nsubj_verb 	 Laterano ... received
nsubj_verb 	 protesters ... had
nsubj_verb 	 fingers ... amputated
nsubj_verb 	 people ... occupied
nsubj_verb 	 movement ... held
nsubj_verb 	 people ... took
nsubj_verb 	 movement ... spread
nsubj_verb 	 Occupy ... began
nsubj_verb 	 government ... guarantee
nsubj_verb 	 it ... came
nsubj_verb 	 Police ... remained
nsubj_verb 	 Mexico ... achieve
nsubj_verb 	 it ... gained
nsubj_verb 	 protesters ... failed
nsubj_verb 	 protests ... occurred
nsubj_verb 	 movement ... drew
nsubj_verb 	 protesters ... remained
nsubj_verb 	 Ganbaatar ... announced
nsubj_verb 	 association ... joins
nsubj_verb 	 He ... claimed
nsubj_verb 	 bankers ... charging
nsubj_verb 	 protesters ... gathered
nsubj_verb 	 protesters ... created
nsubj_verb 	 they ... presented
nsubj_verb 	 demands ... investigate
nsubj_verb 	 which ... took
nsubj_verb 	 demands ... focused
nsubj_verb 	 protests ... took
nsubj_verb 	 protests ... began
nsubj_verb 	 protest ... started
nsubj_verb 	 they ... call
nsubj_verb 	 police ... moved
nsubj_verb 	 police ... said
nsubj_verb 	 that ... started
nsubj_verb 	 movement ... ended
nsubj_verb 	 which ... saw
nsubj_verb 	 it ... sells
nsubj_verb 	 movement ... began
nsubj_verb 	 movement ... met
nsubj_verb 	 Times ... described
nsubj_verb 	 group ... has
nsubj_verb 	 who ... invited
nsubj_verb 	 people ... took
nsubj_verb 	 camp ... survived
nsubj_verb 	 group ... occupying
nsubj_verb 	 camp ... lasted
nsubj_verb 	 It ... consists
nsubj_verb 	 groups ... adopted
nsubj_verb 	 Hundreds ... held
nsubj_verb 	 Protesters ... focused
nsubj_verb 	 which ... started
nsubj_verb 	 One ... argued
nsubj_verb 	 Korea ... overcame
nsubj_verb 	 series ... demands
nsubj_verb 	 protesters ... consider
nsubj_verb 	 media ... related
nsubj_verb 	 Movement ... drew
nsubj_verb 	 protesters ... demonstrated
nsubj_verb 	 protesters ... established
nsubj_verb 	 protests ... developed
nsubj_verb 	 which ... featured
nsubj_verb 	 reaction ... caused
nsubj_verb 	 protests ... widen
nsubj_verb 	 people ... finding
nsubj_verb 	 all ... finding
nsubj_verb 	 What ... started
nsubj_verb 	 Demands ... included
nsubj_verb 	 protests ... spread
nsubj_verb 	 protesters ... gathered
nsubj_verb 	 Police ... sealed
nsubj_verb 	 people ... gathered
nsubj_verb 	 canon ... said
nsubj_verb 	 people ... exercise
nsubj_verb 	 protests ... occurred
nsubj_verb 	 camps ... took
nsubj_verb 	 protests ... focused
nsubj_verb 	 Police ... arrested
nsubj_verb 	 who ... occupying
nsubj_verb 	 police ... arrested
nsubj_verb 	 body ... occupied
nsubj_verb 	 camp ... lasted
nsubj_verb 	 police ... swept
nsubj_verb 	 police ... dragging
nsubj_verb 	 Police ... said
nsubj_verb 	 protesters ... remained
nsubj_verb 	 group ... says
nsubj_verb 	 that ... challenge
nsubj_verb 	 Belfast ... initiated
nsubj_verb 	
 Belfast ... took
nsubj_verb 	 It ... took
nsubj_verb 	 Derry ... take
nsubj_verb 	 Coleraine ... took
nsubj_verb 	 group ... protested
nsubj_verb 	 Council ... backed
nsubj_verb 	 Protesters ... set
nsubj_verb 	 council ... obtained
nsubj_verb 	 council ... agreed
nsubj_verb 	 Cardiff ... set
nsubj_verb 	 Cardiff ... set
nsubj_verb 	 protests ... began
nsubj_verb 	 movement ... rejects
nsubj_verb 	 police ... discovered
nsubj_verb 	 march ... resulted
nsubj_verb 	 Police ... used
nsubj_verb 	 march ... received
nsubj_verb 	 protesters ... attempted
nsubj_verb 	 Some ... said
nsubj_verb 	 police ... tricked
nsubj_verb 	 they ... began
nsubj_verb 	 officers ... cleared
nsubj_verb 	 Police ... fired
nsubj_verb 	 organizers ... said
nsubj_verb 	 Olsen ... suffered
nsubj_verb 	 witnesses ... believed
nsubj_verb 	 protesters ... shut
nsubj_verb 	 Police ... estimated
nsubj_verb 	 4,500 ... marched
nsubj_verb 	 police ... cleared
nsubj_verb 	 journalists ... complained
nsubj_verb 	 police ... made
nsubj_verb 	 journalists ... responded
nsubj_verb 	 they ... perceived
nsubj_verb 	 that ... threaten
nsubj_verb 	 McKenzie ... commented
nsubj_verb 	 authorities ... honour
nsubj_verb 	 Homes ... embarked
nsubj_verb 	 they ... say
nsubj_verb 	 who ... made
nsubj_verb 	 that ... took
nsubj_verb 	 movement ... took
nsubj_verb 	 protesters ... returned
nsubj_verb 	 Rousseff ... said
nsubj_verb 	 We ... agree
nsubj_verb 	 movements ... used
nsubj_verb 	 we ... see
nsubj_verb 	 Flaherty ... expressed
nsubj_verb 	 He ... commented
nsubj_verb 	 I ... understand
nsubj_verb 	 Singh ... described
nsubj_verb 	 Khamenei ... voiced
nsubj_verb 	 it ... grow
nsubj_verb 	 it ... bring
nsubj_verb 	 Brown ... said
nsubj_verb 	 who ... say
nsubj_verb 	 we ... build
nsubj_verb 	 people ... take
nsubj_verb 	 they ... do
nsubj_verb 	 they ... reflect
nsubj_verb 	 He ... mentioned
nsubj_verb 	 politics ... speaks
nsubj_verb 	 Council ... set
nsubj_verb 	 We ... regard
nsubj_verb 	 Edinburgh ... stated
nsubj_verb 	 States ... spoke
nsubj_verb 	 authorities ... collaborated
nsubj_verb 	 who ... participated
nsubj_verb 	 authorities ... sent
nsubj_verb 	 administration ... worked
nsubj_verb 	 Venezuela ... condemned
nsubj_verb 	 Affairs ... had
nsubj_verb 	 Fukuyama ... argued
nsubj_verb 	 he ... wrote
nsubj_verb 	 populism ... taken
nsubj_verb 	 survey ... suggested
nsubj_verb 	 movement ... succeeded
nsubj_verb 	 protesters ... lent
nsubj_verb 	 message ... declared
nsubj_verb 	 interests ... cater
nsubj_verb 	 generation ... grown
nsubj_verb 	 we ... have
nsubj_verb 	 Branson ... said
nsubj_verb 	 they ... protesting
nsubj_verb 	 community ... takes
nsubj_verb 	 they ... made
nsubj_verb 	 Jackson ... said
nsubj_verb 	 which ... sweeping
nsubj_verb 	 survey ... found
nsubj_verb 	 many ... reported
nsubj_verb 	 who ... dislike
nsubj_verb 	 Support ... varied
nsubj_verb 	 Australia ... reporting
nsubj_verb 	 impacts ... include
nsubj_verb 	 protests ... helped
nsubj_verb 	 Americans ... face
nsubj_verb 	 that ... burdens
nsubj_verb 	 movement ... appears
nsubj_verb 	 print ... mentioned
nsubj_verb 	 occupation ... began
nsubj_verb 
	 interest ... waned
nsubj_verb 	 movement ... raised
nsubj_verb 	 organizers ... consider
nsubj_verb 	 unions ... become
nsubj_verb 	 they ... employ
nsubj_verb 	 protest ... provided
nsubj_verb 	 Offshoots ... bought
nsubj_verb 	 individuals ... owe
nsubj_verb 	 they ... have
nsubj_verb 	 Jubilee ... claims
nsubj_verb 	 Chomsky ... argues
nsubj_verb 	 movement ... created
nsubj_verb 	 that ... exist
nsubj_verb 	 people ... doing
nsubj_verb 	 Jubilee ... reports
nsubj_verb 	 it ... cleared
nsubj_verb 	 Telegraph ... reported
nsubj_verb 	 members ... voted
nsubj_verb 	 shows ... using
nsubj_verb 	 Office ... made
nsubj_verb 	 City ... added
nsubj_verb 	 Conan ... launched
nsubj_verb 	 Times ... argued
nsubj_verb 	 movement ... had
nsubj_verb 	 commentators ... suggested
nsubj_verb 	 movement ... had
nsubj_verb 	 Economist ... reported
nsubj_verb 	 protesters ... caused
nsubj_verb 	 government ... pass
nsubj_verb 	 banks ... claw
nsubj_verb 	 Deutch ... introduced
nsubj_verb 	 which ... overturn
nsubj_verb 	 Gore ... called
nsubj_verb 	 It ... works
nsubj_verb 	 Mason ... said
nsubj_verb 	 movement ... started
nsubj_verb 	 it ... have
nsubj_verb 	 journalists ... suggested
nsubj_verb 	 Occupy ... influenced
nsubj_verb 	 movement ... creating
nsubj_verb 	 Inequality ... remained
nsubj_verb 	 he ... mentions
nsubj_verb 	 analysts ... say
nsubj_verb 	 which ... reflects
nsubj_verb 	 Occupy ... become
nsubj_verb 	 inequality ... become
nsubj_verb 	 Magazine ... declared
nsubj_verb 	 protests ... began
nsubj_verb 	 FBI ... formed
nsubj_verb 	 Banks ... met
nsubj_verb 	 FBI ... offered
nsubj_verb 	 officials ... met
nsubj_verb 	 officials ... met
nsubj_verb 	 FBI ... used
nsubj_verb 	 which ... gave
nsubj_verb 	 DSAC ... coordinated
nsubj_verb 	 organizations ... filed
nsubj_verb 	 FBI ... withheld
nsubj_verb 	 Shapiro ... sent
nsubj_verb 	 FBI ... refused
nsubj_verb 	 Shapiro ... filed
nsubj_verb 	 document ... confirmed
nsubj_verb 	 it ... opened
nsubj_verb 	 critique ... concerns
nsubj_verb 	 movement ... focused
nsubj_verb 	 that ... differs
nsubj_verb 	 dominance ... becomes
nsubj_verb 	 that ... follow
nsubj_verb 	 Practicality ... dominates
nsubj_verb 	 that ... rationalize
nsubj_verb 	 activists ... seen
nsubj_verb 	 it ... stall
nsubj_verb 	 Dean ... argues
nsubj_verb 	 focus ... paved
nsubj_verb 	 Emphasis ... encouraged
nsubj_verb 	 Celebration ... heightened
nsubj_verb 	 who ... emerged
nsubj_verb 	 anarchism ... suggests
nsubj_verb 	 It ... pushes
nsubj_verb 	 who ... called
nsubj_verb 	 Remarks ... sparked
nsubj_verb 	 many ... observed
nsubj_verb 	 protests ... included
nsubj_verb 	 Jews ... control
nsubj_verb 	 who ... running
nsubj_verb 	 Foxman ... stated
nsubj_verb 	 that ... deals
nsubj_verb 	 you ... going
nsubj_verb 	 that ... believe
nsubj_verb 	 they ... expressing

This returns us the verbs and their nominal subjects.

Note that when defining pattern rules for dependency matching, you can also create new “chains” that start from the anchor pattern.

For example, to find the direct objects (dobj) for the verbs matched above, we should not add this as a link to the existing chain whose rightmost item is currently named subject.

Instead, we need to start a new chain that begins from the anchor pattern verb.

{'LEFT_ID': 'verb', 'REL_OP': '>', 'RIGHT_ID': 'd_object', 'RIGHT_ATTRS': {'DEP': 'dobj'}}

Just as above, we define that this pattern should be on the right-hand side of the pattern verb, essentially starting a new chain.

Furthermore, the pattern verb should govern this node (>) and have the relation dobj. We also name this pattern d_object using the RIGHT_ID attribute.

Let’s define a new pattern and add it to the DependencyMatcher object.

# Define a list with nested dictionaries that contains the pattern to be matched
dep_pattern_2 = [{'RIGHT_ID': 'verb', 'RIGHT_ATTRS': {'POS': 'VERB'}},
                 {'LEFT_ID': 'verb', 'REL_OP': '>', 'RIGHT_ID': 'subject', 'RIGHT_ATTRS': {'DEP': 'nsubj'}},
                 {'LEFT_ID': 'verb', 'REL_OP': '>', 'RIGHT_ID': 'd_object', 'RIGHT_ATTRS': {'DEP': 'dobj'}}
                ]

# Add the pattern to the matcher under the name 'nsubj_verb'
dep_matcher.add('nsubj_verb_dobj', patterns=[dep_pattern_2])

# Apply the DependencyMatcher to the Doc object under 'doc'; Store the result 
# under the variable 'dep_matches'.
dep_matches = dep_matcher(doc)

# Loop over each tuple in the list 'dep_matches'
for match in dep_matches:
    
    # Take the first item in the tuple at [0] and assign it under
    # the variable 'pattern_name'. This item is a spaCy Lexeme object.
    pattern_name = match[0]
    
    # Take the second item in the tuple at [1] and assign it under
    # the variable 'matches'. This is a list of indices referring to the
    # Doc object under 'doc' that we just matched.
    matches = match[1]

    # Because we now have two patterns for matching which return lists of
    # different length, e.g. lists with two indices for 'nsubj_verb' and
    # lists with three indices for 'nsubj_verb_dobj', we must now define
    # conditional criteria for handling these lists.
    if len(matches) > 2:
        
        # Let's unpack the matches list into variables for clarity
        verb, subject, dobject = matches[0], matches[1], matches[2]
    
        # Print the matches by first fetching the name of the pattern from the 
        # Vocabulary object. Next, use the 'subject' and 'verb' variables to 
        # index the Doc object. This gives us the actual Tokens matched. Use a
        # tabulator ('\t') and some stops ('...') to separate the output.
        print(nlp.vocab[pattern_name].text, '\t', doc[subject], '...', doc[verb], '...', doc[dobject])
        
    # Alternative condition with just two items in the list.
    else:
        
        # Let's unpack the matches list into variables for clarity
        verb, subject = matches[0], matches[1]
    
        # Print the matches by first fetching the name of the pattern from the 
        # Vocabulary object. Next, use the 'subject' and 'verb' variables to 
        # index the Doc object. This gives us the actual Tokens matched. Use a
        # tabulator ('\t') and some stops ('...') to separate the output.
        print(nlp.vocab[pattern_name].text, '\t', doc[subject], '...', doc[verb])
nsubj_verb 	 that ... expressed
nsubj_verb 	 It ... aimed
nsubj_verb 	 movement ... had
nsubj_verb 	 groups ... had
nsubj_verb 	 concerns ... included
nsubj_verb 	 corporations ... control
nsubj_verb 	 that ... benefited
nsubj_verb 	 It ... formed
nsubj_verb 	 Steger ... called
nsubj_verb 	 Occupy ... began
nsubj_verb 	 protests ... taken
nsubj_verb 	 movement ... became
nsubj_verb 	 protests ... started
nsubj_verb 	 repression ... remained
nsubj_verb 	 this ... began
nsubj_verb 	 police ... attempted
nsubj_verb 	 authorities ... cleared
nsubj_verb 	 movement ... uses
nsubj_verb 	 it ... organizes
nsubj_verb 	 West ... described
nsubj_verb 	 Director ... stated
nsubj_verb 	 students ... occupied
nsubj_verb 	 that ... resulted
nsubj_verb 	 slogan ... emerged
nsubj_verb 	 Post ... noted
nsubj_verb 	 that ... read
nsubj_verb 	 who ... designed
nsubj_verb 	 White ... traveled
nsubj_verb 	 He ... wrote
nsubj_verb 	 movement ... began
nsubj_verb 	 camping ... marked
nsubj_verb 	 leader ... called
nsubj_verb 	 Foundation ... proposed
nsubj_verb 	 Lasn ... registered
nsubj_verb 	 we ... floated
nsubj_verb 	 it ... snowballed
nsubj_verb 	 Village ... set
nsubj_verb 	 protest ... received
nsubj_verb 	 group ... encouraged
nsubj_verb 	 They ... promoted
nsubj_verb 	 It ... refers
nsubj_verb 	 percent ... tripled
nsubj_verb 	 incomes ... grew
nsubj_verb 	 % ... saw
nsubj_verb 	 income ... decreased
nsubj_verb 	 that ... increased
nsubj_verb 	 taxation ... became
nsubj_verb 	 earners ... saw
nsubj_verb 	 income ... increase
nsubj_verb 	 % ... owned
nsubj_verb 	 % ... owned
nsubj_verb 	 % ... owned
nsubj_verb 	 % ... owning
nsubj_verb 	 which ... started
nsubj_verb 	 share ... grew
nsubj_verb 	 that ... grew
nsubj_verb 	 Recession ... caused
nsubj_verb 	 income ... grew
nsubj_verb 	 % ... went
nsubj_verb 	 who ... had
nsubj_verb 	 that ... indicate
nsubj_verb 	 Lasn ... said
nsubj_verb 	 that ... allowed
nsubj_verb 	 movement ... grow
nsubj_verb 	 Adbusters ... trying
nsubj_verb 	 Wolf ... argued
nsubj_verb 	 Wolf ... argued
nsubj_verb 	 they ... have
nsubj_verb 	 they ... saw
nsubj_verb 	 magazine ... stated
nsubj_verb 	 protesters ... wanted
nsubj_verb 	 commentators ... criticized
nsubj_verb 	 movement ... defined
nsubj_verb 	 they ... argued
nsubj_verb 	 movement ... seeks
nsubj_verb 	 contingent ... released
nsubj_verb 	 they ... called
nsubj_verb 	 it ... takes
nsubj_verb 	 Occupy ... said
nsubj_verb 	 they ... working
nsubj_verb 	 that ... reflected
nsubj_verb 	 Activists ... used
nsubj_verb 	 Indymedia ... helped
nsubj_verb 	 provider ... offered
nsubj_verb 	 movement ... went
nsubj_verb 	 Fund ... released
nsubj_verb 	 that ... strip
nsubj_verb 	 Homes ... embarked
nsubj_verb 	 who ... lost
nsubj_verb 	 they ... called
nsubj_verb 	 that ... took
nsubj_verb 	 group ... planned
nsubj_verb 	 Much ... occurs
nsubj_verb 	 This ... features
nsubj_verb 	 who ... comment
nsubj_verb 	 anyone ... join
nsubj_verb 	 Street ... uses
nsubj_verb 	 they ... belong
nsubj_verb 	 women ... get
nsubj_verb 	 males ... wait
nsubj_verb 	 turn ... speak
nsubj_verb 	 movement ... began
nsubj_verb 	 which ... premiered
nsubj_verb 	 Sharp ... warned
nsubj_verb 	 movement ... employing
nsubj_verb 	 he ... said
nsubj_verb 	 protesters ... have
nsubj_verb 	 they ... achieve
nsubj_verb 	 they ... think
nsubj_verb 	 they ... change
nsubj_verb 	 Protest ... accomplishes
nsubj_verb 	 Castells ... congratulated
nsubj_verb 	 Castells ... said
nsubj_verb 	 it ... help
nsubj_verb 	 them ... gain
nsubj_verb 	 they ... make
nsubj_verb 	 Group ... endorsed
nsubj_verb 	 occupiers ... upheld
nsubj_verb 	 journalists ... saying
nsubj_verb 	 branch ... accept
nsubj_verb 	 who ... signed
nsubj_verb 	 Klein ... congratulated
nsubj_verb 	 sources ... began
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nsubj_verb 
	 who ... sought
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 ... coordinated
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nsubj_verb_dobj 	 leader ... named ... it
nsubj_verb_dobj 	 demonstrations ... took ... place
nsubj_verb_dobj 	 demonstrations ... took ... towns
nsubj_verb_dobj 	 protesters ... defied ... orders
nsubj_verb_dobj 	 Sydney ... had ... occupation
nsubj_verb_dobj 	 demonstration ... took ... place
nsubj_verb_dobj 	 movement ... had ... gathering
nsubj_verb_dobj 	 people ... attended ... corner
nsubj_verb_dobj 	 Three ... took ... place
nsubj_verb_dobj 	 one ... took ... place
nsubj_verb_dobj 	 protests ... included ... camping
nsubj_verb_dobj 	 They ... received ... visit
nsubj_verb_dobj 	 protests ... taken ... place
nsubj_verb_dobj 	 people ... occupied ... Park
nsubj_verb_dobj 	 people ... occupied ... front
nsubj_verb_dobj 	 group ... occupied ... Park
nsubj_verb_dobj 	 Police ... dissolved ... camp
nsubj_verb_dobj 	 protesters ... started ... Camp
nsubj_verb_dobj 	 movement ... used ... OccupyBufferZ
nsubj_verb_dobj 	 police ... joined ... them
nsubj_verb_dobj 	 people ... joined ... occupation
nsubj_verb_dobj 	 Occupy ... took ... residence
nsubj_verb_dobj 	 Berlin ... established ... camp
nsubj_verb_dobj 	 protests ... took ... place
nsubj_verb_dobj 	 HSBC ... filed ... lawsuit
nsubj_verb_dobj 	 protesters ... leave ... area
nsubj_verb_dobj 	 Laterano ... received ... damage
nsubj_verb_dobj 	 people ... occupied ... Croce
nsubj_verb_dobj 	 movement ... held ... assembly
nsubj_verb_dobj 	 people ... took ... part
nsubj_verb_dobj 	 government ... guarantee ... access
nsubj_verb_dobj 	 Mexico ... achieve ... level
nsubj_verb_dobj 	 movement ... drew ... thousands
nsubj_verb_dobj 	 bankers ... charging ... rates
nsubj_verb_dobj 	 protesters ... created ... set
nsubj_verb_dobj 	 they ... presented ... which
nsubj_verb_dobj 	 which ... took ... place
nsubj_verb_dobj 	 protests ... took ... place
nsubj_verb_dobj 	 they ... call ... what
nsubj_verb_dobj 	 which ... saw ... restoration
nsubj_verb_dobj 	 Times ... described ... movement
nsubj_verb_dobj 	 group ... has ... structure
nsubj_verb_dobj 	 who ... invited ... Democracy
nsubj_verb_dobj 	 people ... took ... part
nsubj_verb_dobj 	 group ... occupying ... amenity
nsubj_verb_dobj 	 groups ... adopted ... Occupy4FreeEducation
nsubj_verb_dobj 	 Hundreds ... held ... rallies
nsubj_verb_dobj 	 Korea ... overcame ... crisis
nsubj_verb_dobj 	 series ... demands ... change
nsubj_verb_dobj 	 media ...
 related ... protests
nsubj_verb_dobj 	 media ... related ... generation
nsubj_verb_dobj 	 Movement ... drew ... inspiration
nsubj_verb_dobj 	 protesters ... established ... occupation
nsubj_verb_dobj 	 which ... featured ... use
nsubj_verb_dobj 	 people ... finding ... reason
nsubj_verb_dobj 	 all ... finding ... reason
nsubj_verb_dobj 	 Demands ... included ... end
nsubj_verb_dobj 	 Police ... sealed ... entrance
nsubj_verb_dobj 	 people ... exercise ... right
nsubj_verb_dobj 	 camps ... took ... place
nsubj_verb_dobj 	 Police ... arrested ... members
nsubj_verb_dobj 	 who ... occupying ... hotel
nsubj_verb_dobj 	 police ... arrested ... people
nsubj_verb_dobj 	 body ... occupied ... Tower
nsubj_verb_dobj 	 police ... dragging ... protesters
nsubj_verb_dobj 	 that ... challenge ... system
nsubj_verb_dobj 	 Belfast ... initiated ... protest
nsubj_verb_dobj 	 Belfast ... took ... residence
nsubj_verb_dobj 	 It ... took ... control
nsubj_verb_dobj 	 Derry ... take ... place
nsubj_verb_dobj 	 Coleraine ... took ... University
nsubj_verb_dobj 	 Coleraine ... took ... Room
nsubj_verb_dobj 	 group ... protested ... demolition
nsubj_verb_dobj 	 Council ... backed ... Edinburgh
nsubj_verb_dobj 	 council ... obtained ... order
nsubj_verb_dobj 	 Cardiff ... set ... site
nsubj_verb_dobj 	 Cardiff ... set ... camp
nsubj_verb_dobj 	 movement ... rejects ... institutions
nsubj_verb_dobj 	 police ... discovered ... site
nsubj_verb_dobj 	 Police ... used ... technique
nsubj_verb_dobj 	 Police ... used ... use
nsubj_verb_dobj 	 march ... received ... coverage
nsubj_verb_dobj 	 police ... tricked ... protesters
nsubj_verb_dobj 	 officers ... cleared ... sites
nsubj_verb_dobj 	 Police ... fired ... canisters
nsubj_verb_dobj 	 Olsen ... suffered ... fracture
nsubj_verb_dobj 	 protesters ... shut ... Port
nsubj_verb_dobj 	 police ... cleared ... encampment
nsubj_verb_dobj 	 police ... made ... decision
nsubj_verb_dobj 	 they ... perceived ... what
nsubj_verb_dobj 	 that ... threaten ... protections
nsubj_verb_dobj 	 authorities ... honour ... obligation
nsubj_verb_dobj 	 who ... made ... billions
nsubj_verb_dobj 	 that ... took ... advantage
nsubj_verb_dobj 	 movement ... took ... crisis
nsubj_verb_dobj 	 movements ... used ... that
nsubj_verb_dobj 	 movements ... used ... demonstrations
nsubj_verb_dobj 	 Flaherty ... expressed ... sympathy
nsubj_verb_dobj 	 I ... understand ... frustration
nsubj_verb_dobj 	 Singh ... described ... protests
nsubj_verb_dobj 	 Khamenei ... voiced ... support
nsubj_verb_dobj 	 Khamenei ... voiced ... Kingdom
nsubj_verb_dobj 	 it ... bring ... system
nsubj_verb_dobj 	 we ... build ... system
nsubj_verb_dobj 	 people ... take ... risks
nsubj_verb_dobj 	 they ... reflect ... crisis
nsubj_verb_dobj 	 Council ... set ... precedent
nsubj_verb_dobj 	 We ... regard ... this
nsubj_verb_dobj 	 Venezuela ... condemned ... repression
nsubj_verb_dobj 	 Affairs ... had ... articles
nsubj_verb_dobj 	 populism ... taken ... form
nsubj_verb_dobj 	 protesters ... lent ... support
nsubj_verb_dobj 	 we ... have ... future
nsubj_verb_dobj 	 community ... takes ... some
nsubj_verb_dobj 	 they ... made ... difference
nsubj_verb_dobj 	 which ... sweeping ... nation
nsubj_verb_dobj 	 many ... reported ... response
nsubj_verb_dobj 	 who ... dislike ... it
nsubj_verb_dobj 	 Australia ... reporting ... lowest
nsubj_verb_dobj 	 impacts ... include ... following
nsubj_verb_dobj 	 that ... burdens ... 
class
nsubj_verb_dobj 	 print ... mentioned ... inequality
nsubj_verb_dobj 	 movement ... raised ... awareness
nsubj_verb_dobj 	 organizers ... consider ... what
nsubj_verb_dobj 	 organizers ... consider ... wealth
nsubj_verb_dobj 	 protest ... provided ... hundreds
nsubj_verb_dobj 	 Offshoots ... bought ... millions
nsubj_verb_dobj 	 individuals ... owe ... that
nsubj_verb_dobj 	 they ... have ... means
nsubj_verb_dobj 	 movement ... created ... something
nsubj_verb_dobj 	 people ... doing ... things
nsubj_verb_dobj 	 it ... cleared ... million
nsubj_verb_dobj 	 shows ... using ... term
nsubj_verb_dobj 	 shows ... using ... %
nsubj_verb_dobj 	 Office ... made ... references
nsubj_verb_dobj 	 City ... added ... word
nsubj_verb_dobj 	 Conan ... launched ... contest
nsubj_verb_dobj 	 movement ... had ... impact
nsubj_verb_dobj 	 movement ... had ... support
nsubj_verb_dobj 	 government ... pass ... laws
nsubj_verb_dobj 	 which ... overturn ... decision
nsubj_verb_dobj 	 it ... have ... profile
nsubj_verb_dobj 	 Occupy ... influenced ... State
nsubj_verb_dobj 	 movement ... creating ... space
nsubj_verb_dobj 	 he ... mentions ... movement
nsubj_verb_dobj 	 which ... reflects ... fact
nsubj_verb_dobj 	 Magazine ... declared ... Triumph
nsubj_verb_dobj 	 FBI ... formed ... Council
nsubj_verb_dobj 	 FBI ... offered ... plans
nsubj_verb_dobj 	 FBI ... used ... informants
nsubj_verb_dobj 	 FBI ... used ... information
nsubj_verb_dobj 	 which ... gave ... updates
nsubj_verb_dobj 	 organizations ... filed ... suits
nsubj_verb_dobj 	 FBI ... withheld ... documents
nsubj_verb_dobj 	 Shapiro ... sent ... requests
nsubj_verb_dobj 	 FBI ... refused ... request
nsubj_verb_dobj 	 Shapiro ... filed ... complaint
nsubj_verb_dobj 	 document ... confirmed ... plot
nsubj_verb_dobj 	 it ... opened ... investigation
nsubj_verb_dobj 	 critique ... concerns ... itself
nsubj_verb_dobj 	 movement ... focused ... demands
nsubj_verb_dobj 	 that ... differs ... little
nsubj_verb_dobj 	 focus ... paved ... way
nsubj_verb_dobj 	 Emphasis ... encouraged ... people
nsubj_verb_dobj 	 Celebration ... heightened ... skepticism
nsubj_verb_dobj 	 It ... pushes ... us
nsubj_verb_dobj 	 Remarks ... sparked ... criticism
nsubj_verb_dobj 	 protests ... included ... slogans
nsubj_verb_dobj 	 Jews ... control ... Street
nsubj_verb_dobj 	 who ... running ... banks

As the output shows, the pattern nsubj_verb_dobj returns both nominal subjects and direct objects of the verbs, which we defined using different “chains” of the pattern.

We could easily add another chain to the anchor pattern, for example, to search for prepositional phrases, or add further links to either of the existing chains to search for some more fine-grained features.

Examining matches in context using concordances

We can examine matches in their context of occurrence using concordances. In corpus linguistics, concordances are often understood as lines of text that show a match in its context of occurrence.

These concordance lines can help understand why and how a particular token or structure is used in given context.

To create concordance lines using spaCy, let’s start by importing the Printer class from wasabi, which is a small Python library that spaCy uses for colouring and formatting messages. We will use wasabi to highlight the matches in the concordance lines.

We first initialise a Printer object, which we then assign under the variable match. Next, we test the Printer object by printing some text in red colour.

# Import the Printer class from wasabi
from wasabi import Printer

# Initialise a Printer object; assign the object under the variable 'match'
match = Printer()

# Use the Printer to print out some text in red colour
match.text('Hello world!', color='red')
Hello world!

We then proceed to loop over the results returned by the Matcher object morph_matcher. As we learned above, the results consist of Span objects in a list, which are stored under the variable morph_results.

We loop over items in this list and use the enumerate() function to keep track of their count. We also provide the argument start with the value 1 to the enumerate() function to start counting from the number 1.

During the loop, we refer to this count using the variable i and to the Span object as result. The number under i is incremented with every Span object.

We then print out the following output for each Span object in the list morph_results:

  1. i: The number of the item in the list.

  2. doc[result.start - 7: result.start]: A slice of the Doc object stored under the variable doc, which we searched for matches. As usual, we define a slice using brackets and separate the start and end of a slice using a colon. We take a slice that begins 7 Tokens before the start of the match (result.start - 7), and terminates at the start of the match result.start.

  3. match.text(result, color="red", no_print=True): The matching Span object, rendered using the wasabi Printer object match in red colour. We also set the argument no_print to True to prevent wasabi from printing the output on a new line.

  4. doc[result.end: result.end + 7]: Another slice of the Doc object stored under the variable doc. Here we take a slice that begins at the end of the match result.end and terminates 7 Tokens after the end of the match (result.end + 7).

Essentially, we use the indices available under start and end attributes of each Span to retrieve the linguistic context in which the Span occurs.

# Loop over the matches in 'morph_results' and keep count of items
for i, result in enumerate(morph_results, start=1):
    
    # Print following information for each match
    print(i,  # Item number being looped over
          doc[result.start - 7: result.start],  # The slice of the Doc preceding the match
          match.text(result, color='red', no_print=True),  # The match, rendered in red colour using wasabi
          doc[result.end: result.end + 7]  # The slice of the Doc following the match
         )
1 unity among the "99%".The Community Environmental Legal Defense Fund released a model community bill of rights,
2 raid was chaotic and violent, but Oakland Police Chief Howard Jordan expressed his pleasure concerning the operation because neither
3 process.In March 2012, former U.S. Vice President Al Gore called on activists to "occupy democracy"
4 few demands. On 12 October 2011 Los Angeles City Council became one of the first governmental bodies in
5 by bullhorn, after reviewing it, Judge Jed S. Rakoff sided with plaintiffs, saying, "a
6 other countries."
Canada— Finance Minister Jim Flaherty expressed sympathy with the protests, stating "
7 of that."
India— Prime Minister Manmohan Singh described the protests as "a warning for
8 of governance".
Iran— Supreme Leader Ayatollah Khamenei voiced his support for the Occupy Movement saying
9 —On 21 October 2011, former Prime Minister Gordon Brown said the protests were about fairness. "
10 Abraham Foxman, national director of the Anti-Defamation League stated that "it's not surprising that
11 . The Direct Action Working Group of Occupy Wall Street endorsed diversity of tactics from the earliest days
12 march across the Brooklyn Bridge. The New York Times reported that more than 700 arrests were made
13 A. Myerson, a media coordinator for Occupy Wall Street said , "The cops watched and did
14 on 18 November 2011, campus police Lieutenant John Pike used pepper spray on seated students. The
15 Economic Forum. On 17 March, Occupy Wall Street attempted to mark six months of the movement
16 clock until 23 July 2013, when Mayor Charlie Hales ordered the removal of the vigil and associated
17 The Roman Catholic church Santi Marcellino e Pietro al Laterano received extensive damage, including a statue of
18 an environmentalist protest against plans to replace Taksim Gezi Park developed into wider anti-government demonstrations.
19 Executive Director Alison Bethel McKenzie of the International Press Institute commented : "It is completely unacceptable to
20 of the occupation.

Brazil— President Dilma Rousseff said , "We agree with some of
21 . On Saturday 26 November 2011, Edinburgh City Council set a worldwide precedent by voting in favour
22 Occupy Edinburgh.
United States— President Barack Obama spoke in support of the movement, but
23 City, Portland, Oakland, and New York City sent in police to crack down on the
24 removed by police.
Venezuela— President Hugo Chávez condemned the "horrible repression" of the
25 In January 2012, members of the American Dialect Society voted with an overwhelming majority for "Occupy
26 instability. It formed part of what Manfred Steger called the "global justice movement".The
27 Washington Post, the movement, which Cornel West described as a "democratic awakening",
28 Nothing' first emerged." The Huffington Post noted that "During one incident in March
29 financial crisis. Adbusters co-founder Kalle Lasn registered the OccupyWallStreet.org web address on 9 June
30 the inspirations for the movement was the Democracy Village set up in 2010, outside the British
31 Hood tax planned for 29 October. Naomi Wolf argued that the impression created by much of
32 Some commentators such as David Graeber and Judith Butler criticized the idea that the movement must have
33 . The progressive provider May First/ People Link offered cost-free memberships for dozens of
34 ."In late May 2011, sociologist Manuel Castells congratulated Spanish occupiers for the fact that not
35 female protestors. In early October, Naomi Klein congratulated New York occupiers for their commitment to
36 wished to stay. Rick Hampton for USA Today said the vast majority of occupy members have
37 the global movement in December 2011, Anthony Barnett said its nonviolence remained an immense strength.
38 the Occupy Wall Street Movement, but Kanye West justified his appearance as helping give power back
39 Yoko Ono, Mark Ruffalo, and Michael Moore tweeted and showed their support.
Many
40 .

The WikiLeaks endorsed news site WikiLeaks Central began promoting the idea of a "US
41 , and the American WikiLeaks Central writer Alexa O'Brien modeled the concept after the Day of Rages
42 a forcible eviction. Financial Times editor Richard Lambert suggested that the shift to confrontational tactics by
43 of the movement, Financial Times journalist Shannon Bond found that issues of concern included: "
44 18 months. On 22 December The Washington Post reported that some of the cities which had
45 .

On 2 January 2012, Occupy Nigeria began , sparked by Nigeria's President Goodluck
46 shutting down whole cities. On 16 January Jonathan responded by announcing he would bring prices back
47 relief to the New York area since Hurricane Sandy hit , Occupy London's Occupy Economics group
48 April 2016, hundreds of supporters of Bernie Sanders protested outside of CNN's Headquarters in Los
49 hiatus in activism on location, the Occupy Movement organized the Occupy ICE phase in order to
50 .On August 19, 2018, Occupy Kalamazoo began an encampment in Bronson Park to address
51 -occupying multiple sites since.
 Occupy Sydney had an ongoing occupation in Martin Place since
52 Klárov" in Prague was started. Pirate Party participated in the occupation. Police dissolved the
53 ", a permanent occupation of the United Nations controlled buffer zone in the centre of the
54 of the European Central Bank, and Occupy Berlin established a protest camp at St. Mary's
55 . On 13 August 2012, the High Court ruled that the protesters must leave the occupied
56 Cork, Limerick and Galway. The Irish Times described the movement in the following terms:
57 protest, many of the catchphrases of Occupy Seoul contained anti-government or anti-American
58 of the observers has argued that " South Korea overcame the 2008 financial crisis relatively well and
59 riots in 2009. The 15- M Movement drew inspiration from 2011 revolutions in Tunisia,
60 Wales. On 8 January 2012, Lancaster Police arrested four members of Occupy Lancaster who were
61 ".

In Northern Ireland, Occupy Belfast initiated its protest outside the offices of Invest
62 Invest NI on 21 October 2011. Occupy Belfast took residence at Writer's Square, in
63 place in the near future.
 Occupy Coleraine took over the University of Ulster Common Room
64 the Occupy movement worldwide. Protesters from Occupy Glasgow set up in the civic George Square on
65 and demonstrations outside Cardiff magistrates court. Occupy Cardiff set up a new camp in the city
66 the January/February 2012 issue, Francis Fukuyama argued that the Occupy movement was not as
67 survey for the think tank Center for American Progress suggested that the Occupy movement has succeeded in
68 In early December 2011, business magnate Richard Branson said the movement is a "good start
69 difference.On 15 December 2011, Jesse Jackson said that Jesus Christ, Gandhi, and
70 .On 10 November 2011, The Daily Telegraph reported that the word "occupy" had
71 

On 27 December 2011, the Financial Times argued that the movement had had a global
72 ." Also in November 2011, Paul Mason said that the Occupy movement had started to
73 part of the political discourse and The Atlantic Magazine declared "The Triumph of Occupy Wall Street
74 of Financial Stability at the Bank of England stated that the protesters were right to criticise
75 2010, students across the University of California occupied campus buildings in protest against budget cuts
76 some journalists and commentators the camping in Spain marked the start of the global occupy movement
77 additional attention when the internet hacker group Anonymous encouraged its followers to take part in the
78 the top 400 income earners in the U.S. saw their income increase 392% and their
79 not have clear demands was false. Wolf argued that they did have clear demands including
80 , and Meetup to coordinate events. Indymedia helped the movement with communications, saying there
81 It showed 40% of users produced Occupy related content during peak activity of the movement
82 presidential candidates over others.

The WikiLeaks endorsed news site WikiLeaks Central began promoting the
83 . A list of events for 15 October included 951 cities in 82 countries. On
84 FT, offered a different view. Gapper said that it may be advantageous that the
85 to one. In late January, Occupy protested at the World Economic Forum. On
86 concerns'. On 25 June, Feds ordered the protestors to vacate government environs or
87 fact that the protesters were peaceful, HSBC filed a lawsuit for their eviction. On
88 Italian cities the same day. In Rome masked and hooded militants wearing makeshift body armor
89 Kelantan with Occupy Kota Bharu.

 Occupy began in Mexico City on 11 October 2011
90 Nigeria.

The Occupy movement in Norway began on 15 October with protests in Oslo
91 's Time for Outrage!, the NEET troubled generation and current protests in the Middle
92 government demonstrations. Demands issued on 4 June included 

the end of police brutality,
93 in July 2012, the City of Vancouver added the word to its list of reserve
94 In December 2012, the Television show Conan launched a contest called "Occupy Conan"
95 President Joe Biden, have suggested that Occupy influenced the President's January 2012 State of
96 collected by corporate security, and the FBI offered to bank officials its plans to prevent
97 Zions Bank about planned protests. The FBI used informants to infiltrate and monitor protests;
98 with private corporate security officials. The FBI withheld documents requested under the FOIA citing the
99 suppressed sniper rifles". When the FBI refused the request, Shapiro filed a federal
100 When the FBI refused the request, Shapiro filed a federal complaint in Washington, D.C.

This returns a set of concordance lines highlighting the matches in their context of occurrence.

Note that in some cases, the preceding or following Tokens consist of line breaks indicating a paragraph break, which causes the output to jump a row or two.

This section should have given you an idea of how to search linguistic annotations for matching structures using spaCy.

In the following section, you will be introduced to word embeddings, a technique for approximating the meaning of words.