CODEWITHSUNDEEP
nlpnltkpos-tagger
Adem Youssef
Adem Youssef

Reputation: 67

How to extract phrases from text using specific noun-verb-noun NLTK PoS tag patterns?

I have a data frame that has a column containing some text.

I want to extract phrases from the text with the format NN + VB + NN or NN + NN + VB + NN or NN + ... + NN + VB + NN et cetera. Basically, I want to get the simple phrases with 1 to n nouns before the first encountered verb, followed by a noun.

I'm using nltk.pos_tag after tokenizing the texts to get the tag of each word, however I cannot find a way to get what I want.

I also thought about bigrams, trigrams, ngrams etc. but couldn't find a way to apply it.

Any help, please?

Upvotes: 1

Views: 593

Answers (1)

Kyle F. Hartzenberg
Kyle F. Hartzenberg

Reputation: 3730

Here is a solution which utilises nltk.RegexParser with a custom grammar rule to match occurrences of any numbers of nouns, followed by a verb, followed by a noun, specifically:

{<N.*>+<V.*><N.*>} 

which is equivalent to,

{<NN|NNS|NNP|NNPS>+<VB|VBP|VBZ|VBG|VBD|VBN><NN|NNS|NNP|NNPS>}

Example

Parsing "Prodikos Socrates recommended Plato, and Plato recommended Aristotle" produces the following labelled parse tree:

nouns-verb-noun-example

Output:

['Prodikos', 'Socrates', 'recommended', 'Plato']
['Plato', 'recommended', 'Aristotle']

Note: The above rule does not handle symbols and punctuation interrupting the first sequence nouns (e.g. "Prodikos, Socrates recommended Plato" will only match "Socrates recommended Plato"). There is likely a way to handle this case using some regexp pattern and the NLTK PoS tags but it is not immediately obvious to me.

Solution

from nltk import word_tokenize, pos_tag, RegexpParser

# Text for testing
text = "Prodikos Socrates recommended Plato, and Plato recommended Aristotle"

tokenized = word_tokenize(text)  # Tokenize text
tagged = pos_tag(tokenized)  # Tag tokenized text with PoS tags
print(tagged)
# Output: [('Prodikos', 'NNP'), ('Socrates', 'NNP'), ('recommended', 'VBD'), ('Plato', 'NNP'), (',', ','),
# ('and', 'CC'), ('Plato', 'NNP'), ('recommended', 'VBD'), ('Aristotle', 'NNP')]

# Create custom grammar rule to label occurrences of any number of nouns, followed by a verb, followed by a noun
my_grammar = r"""
NOUNS_VERB_NOUN: {<N.*>+<V.*><N.*>}"""


# Function to create parse tree using custom grammar rules and PoS tagged text
def get_parse_tree(grammar, pos_tagged_text):
    cp = RegexpParser(grammar)
    parse_tree = cp.parse(pos_tagged_text)
    parse_tree.draw()  # Visualise parse tree
    return parse_tree


# Function to get labels from custom grammar:
# takes line separated NLTK regexp grammar rules
def get_labels_from_grammar(grammar):
    labels = []
    for line in grammar.splitlines()[1:]:
        labels.append(line.split(":")[0])
    return labels


# Function takes parse tree & list of NLTK custom grammar labels as input
# Returns phrases which match
def get_phrases_using_custom_labels(parse_tree, custom_labels_to_get):
    matching_phrases = []
    for node in parse_tree.subtrees(filter=lambda x: any(x.label() == custom_l for custom_l in custom_labels_to_get)):
        # Get phrases only, drop PoS tags
        matching_phrases.append([leaf[0] for leaf in node.leaves()])
    return matching_phrases


text_parse_tree = get_parse_tree(my_grammar, tagged)
my_labels = get_labels_from_grammar(my_grammar)
phrases = get_phrases_using_custom_labels(text_parse_tree, my_labels)

for phrase in phrases:
    print(phrase)
# Output:
# ['Prodikos', 'Socrates', 'recommended', 'Plato']
# ['Plato', 'recommended', 'Aristotle']

Upvotes: 1

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