Apply collocation from listo of bigrams with nltk in python

When working with natural language processing tasks, it is often necessary to identify collocations, which are sequences of words that commonly occur together. In Python, the Natural Language Toolkit (NLTK) provides a convenient way to apply collocation analysis to a list of bigrams. In this article, we will explore three different approaches to solve this problem using NLTK and Python.

Approach 1: Using NLTK’s BigramCollocationFinder

The first approach involves using NLTK’s BigramCollocationFinder class to identify collocations from a list of bigrams. Here is the code:


import nltk
from nltk.collocations import BigramCollocationFinder

# List of bigrams
bigrams = [("natural", "language"), ("language", "processing"), ("collocation", "analysis")]

# Create a frequency distribution of the bigrams
finder = BigramCollocationFinder.from_documents(bigrams)

# Get the top 10 collocations
collocations = finder.nbest(nltk.collocations.BigramAssocMeasures().pmi, 10)

# Print the collocations
for collocation in collocations:
    print(collocation)

This approach uses the BigramCollocationFinder class to create a frequency distribution of the bigrams. It then applies the Pointwise Mutual Information (PMI) measure to rank the collocations and selects the top 10. Finally, it prints the collocations.

Approach 2: Using NLTK’s BigramAssocMeasures

The second approach involves using NLTK’s BigramAssocMeasures class to calculate the collocation scores for the bigrams. Here is the code:


import nltk
from nltk.collocations import BigramAssocMeasures

# List of bigrams
bigrams = [("natural", "language"), ("language", "processing"), ("collocation", "analysis")]

# Calculate the collocation scores for the bigrams
scores = {bigram: BigramAssocMeasures.pmi(*bigram) for bigram in bigrams}

# Sort the bigrams based on the scores
sorted_bigrams = sorted(scores, key=scores.get, reverse=True)

# Get the top 10 collocations
collocations = sorted_bigrams[:10]

# Print the collocations
for collocation in collocations:
    print(collocation)

This approach calculates the collocation scores for the bigrams using the PMI measure. It then sorts the bigrams based on the scores and selects the top 10. Finally, it prints the collocations.

Approach 3: Using NLTK’s collocations module

The third approach involves using NLTK’s collocations module, which provides a higher-level interface for collocation analysis. Here is the code:


import nltk
from nltk.collocations import BigramCollocationFinder
from nltk.metrics import BigramAssocMeasures

# List of bigrams
bigrams = [("natural", "language"), ("language", "processing"), ("collocation", "analysis")]

# Create a BigramCollocationFinder object
finder = BigramCollocationFinder.from_documents(bigrams)

# Apply the PMI measure to rank the collocations
collocations = finder.score_ngrams(BigramAssocMeasures.pmi)

# Get the top 10 collocations
top_collocations = collocations[:10]

# Print the collocations
for collocation in top_collocations:
    print(collocation)

This approach creates a BigramCollocationFinder object from the list of bigrams. It then applies the PMI measure to rank the collocations and selects the top 10. Finally, it prints the collocations.

After analyzing the three approaches, it can be concluded that Approach 3, which uses NLTK’s collocations module, is the better option. It provides a higher-level interface and simplifies the process of applying collocation analysis to a list of bigrams. Additionally, it offers more flexibility and functionality compared to the other two approaches.

Rate this post

3 Responses

    1. I personally find Approach 3 more innovative and promising. It pushes boundaries and offers fresh possibilities. Approach 1 might be safe, but it lacks that wow factor. Lets embrace progress and explore new horizons!

Leave a Reply

Your email address will not be published. Required fields are marked *

Table of Contents