How to extract phrases from corpus using gensim

How to extract phrases from corpus using gensim

To extract phrases from a corpus using Gensim, you can use the Phrases class, which helps identify multi-word phrases (collocations) based on the frequency of co-occurrence of words in the text. Here's a step-by-step guide on how to achieve this:

  1. Install Gensim: If you haven't already, you'll need to install Gensim using the following command:

    pip install gensim

  2. Import Gensim and Prepare Corpus: Import the necessary modules and prepare your corpus as a list of tokenized sentences. Each sentence should be a list of words.

    from gensim.models import Phrases from gensim.models.phrases import Phraser import nltk nltk.download('punkt') # Sample corpus (list of tokenized sentences) corpus = [ ["apple", "juice", "is", "tasty"], ["apple", "juice", "contains", "vitamin", "C"], ["orange", "juice", "is", "refreshing"], # ... ]

  3. Train the Phrases Model: Create a Phrases model using the corpus and train it to identify collocations (phrases).

    # Train the Phrases model phrases = Phrases(corpus, min_count=1, threshold=1, delimiter=b'_')

    In the Phrases constructor, you can adjust parameters like min_count (minimum frequency for collocation), threshold (a lower threshold value means more phrases will be detected), and delimiter (a separator to join the words in a phrase).

  4. Apply Phraser and Extract Phrases: After training the Phrases model, you can apply a Phraser to your corpus to transform the sentences by replacing detected phrases with underscores.

    phraser = Phraser(phrases) corpus_with_phrases = phraser[corpus]

    Now, corpus_with_phrases contains sentences where multi-word phrases are joined using underscores.

  5. Inspect the Extracted Phrases: You can inspect the phrases that were extracted using the Phrases model.

    for phrase, score in phrases.export_phrases(corpus_with_phrases): print(phrase.decode('utf-8'), score)

These steps illustrate how to extract phrases from a corpus using Gensim's Phrases model. Adjust the parameters and preprocessing steps to fit your specific use case and data.

Examples

  1. How to extract phrases from a corpus using gensim's Phrases model in Python?

    Description: This query focuses on using gensim's Phrases model to automatically detect and extract phrases from a given corpus.

    from gensim.models.phrases import Phrases, Phraser from gensim.test.utils import common_texts # Create a Phrases model phrases_model = Phrases(common_texts) # Extract phrases from a given sentence sentence = ["apple", "pie", "is", "delicious"] phrases = phrases_model[sentence] print(list(phrases))

  2. How to adjust the threshold for phrase extraction using gensim's Phrases model in Python?

    Description: This query is about adjusting the threshold parameter in gensim's Phrases model to control the strictness of phrase extraction.

    from gensim.models.phrases import Phrases, Phraser from gensim.test.utils import common_texts # Create a Phrases model with a custom threshold phrases_model = Phrases(common_texts, threshold=0.5) # Extract phrases from a given sentence sentence = ["apple", "pie", "is", "delicious"] phrases = phrases_model[sentence] print(list(phrases))

  3. How to extract multi-word expressions from a corpus using gensim's Phrases model in Python?

    Description: This query focuses on using gensim's Phrases model to identify and extract multi-word expressions (phrases) from a given corpus.

    from gensim.models.phrases import Phrases, Phraser from gensim.test.utils import common_texts # Create a Phrases model phrases_model = Phrases(common_texts) # Extract multi-word expressions from a corpus phrases = phrases_model[common_texts] for phrase in phrases: print(phrase)

  4. How to apply pre-trained word embeddings for phrase extraction using gensim in Python?

    Description: This query is about utilizing pre-trained word embeddings along with gensim's Phrases model for more effective phrase extraction.

    from gensim.models import Word2Vec from gensim.models.phrases import Phrases from gensim.test.utils import common_texts # Train a Word2Vec model on the corpus word2vec_model = Word2Vec(common_texts, min_count=1) # Create a Phrases model with pre-trained embeddings phrases_model = Phrases(common_texts, word2vec=word2vec_model.wv) # Extract phrases from a given sentence sentence = ["apple", "pie", "is", "delicious"] phrases = phrases_model[sentence] print(list(phrases))

  5. How to extract collocations from a corpus using gensim's Phrases model in Python?

    Description: This query focuses on using gensim's Phrases model to identify and extract collocations (phrases that frequently occur together) from a given corpus.

    from gensim.models.phrases import Phrases, Phraser from gensim.test.utils import common_texts # Create a Phrases model phrases_model = Phrases(common_texts, min_count=1, threshold=1) # Extract collocations from a given sentence sentence = ["apple", "pie", "is", "delicious"] collocations = phrases_model[sentence] print(list(collocations))

  6. How to handle bigrams and trigrams extraction using gensim's Phrases model in Python?

    Description: This query is about using gensim's Phrases model to handle both bigrams (two-word phrases) and trigrams (three-word phrases) extraction from a given corpus.

    from gensim.models.phrases import Phrases, Phraser from gensim.test.utils import common_texts # Create a Phrases model for bigrams and trigrams phrases_model = Phrases(common_texts, min_count=1, threshold=1, scoring='npmi') # Extract bigrams and trigrams from a given sentence sentence = ["apple", "pie", "is", "delicious"] phrases = phrases_model[sentence] print(list(phrases))

  7. How to customize phrase scoring in gensim's Phrases model for extraction in Python?

    Description: This query focuses on customizing the scoring mechanism in gensim's Phrases model to control how phrases are extracted from a given corpus.

    from gensim.models.phrases import Phrases, Phraser from gensim.test.utils import common_texts # Create a Phrases model with custom scoring phrases_model = Phrases(common_texts, min_count=1, threshold=1, scoring='default') # Extract phrases from a given sentence sentence = ["apple", "pie", "is", "delicious"] phrases = phrases_model[sentence] print(list(phrases))

  8. How to apply custom filters for phrase extraction using gensim's Phrases model in Python?

    Description: This query is about applying custom filters to control which word combinations are considered as phrases by gensim's Phrases model.

    from gensim.models.phrases import Phrases, Phraser from gensim.test.utils import common_texts # Define custom filters for phrase extraction custom_filters = [lambda x: x.lower() not in {'and', 'or'}] # Create a Phrases model with custom filters phrases_model = Phrases(common_texts, min_count=1, threshold=1, scorer='npmi', common_terms=custom_filters) # Extract phrases from a given sentence sentence = ["apple", "and", "pie", "is", "delicious"] phrases = phrases_model[sentence] print(list(phrases))

  9. How to extract noun phrases from a corpus using gensim's Phrases model in Python?

    Description: This query focuses on using gensim's Phrases model to specifically extract noun phrases (multi-word expressions consisting of a noun and its modifiers) from a given corpus.

    from gensim.models.phrases import Phrases, Phraser from gensim.test.utils import common_texts # Create a Phrases model for noun phrases phrases_model = Phrases(common_texts, min_count=1, threshold=1, scoring='default', common_terms={}) # Extract noun phrases from a given sentence sentence = ["apple", "pie", "is", "delicious"] noun_phrases = phrases_model[sentence] print(list(noun_phrases))

  10. How to visualize extracted phrases from a corpus using gensim's Phrases model in Python?

    Description: This query is about visualizing the extracted phrases from a corpus using gensim's Phrases model, which can help in understanding the effectiveness of phrase extraction.

    import matplotlib.pyplot as plt from gensim.models.phrases import Phrases from gensim.test.utils import common_texts # Create a Phrases model phrases_model = Phrases(common_texts, min_count=1, threshold=1) # Extract phrases from a corpus phrases = phrases_model[common_texts] # Plot the distribution of phrases phrase_lengths = [len(phrase) for phrase in phrases] plt.hist(phrase_lengths, bins=range(max(phrase_lengths)+1)) plt.xlabel('Phrase Length') plt.ylabel('Frequency') plt.title('Distribution of Extracted Phrases') plt.show()

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