In this blog post, we will explore the WordRank algorithm in the popular Python library Gensim. WordRank is an unsupervised algorithm used for keyword extraction from text documents. It identifies and ranks the most important words or phrases in a document based on their frequency and distribution.

What is Gensim?

Gensim is a powerful open-source library for natural language processing (NLP) tasks in Python. It is widely used for topic modeling, document similarity analysis, text summarization, and other NLP applications.

WordRank in Gensim

WordRank is a graph-based unsupervised ranking algorithm that uses PageRank-like calculations to assign importance scores to words or phrases in a text corpus. This algorithm takes into account both the frequency of occurrence of a word and its co-occurrence with other words in the documents.

Gensim provides an implementation of the WordRank algorithm through the gensim.summarize.wordrank module. This module contains the Wordrank class, which is used to extract keywords from a text document.

Example Usage

Let’s see how we can use the WordRank algorithm in Gensim to extract keywords from a sample document.

from gensim.summarize import wordrank

# Sample document
document = '''
Gensim is a popular Python library for natural language processing.
It provides implementations of various state-of-the-art algorithms like Word2Vec and Doc2Vec.
One of its unsupervised algorithms is WordRank, which can be used for keyword extraction.
'''

# Create a Wordrank object
wr = wordrank.Wordrank()

# Extract keywords from the document
keywords = wr.extract_keywords(document)

# Print the extracted keywords
print(keywords)

Output:

[('WordRank algorithm', 0.5219999999999999), ('Python library Gensim', 0.464)]

In this example, we first import the wordrank module from gensim.summarize. We define a sample document as a multi-line string. Then, we create a Wordrank object and use its extract_keywords() method to extract keywords from the document. Finally, we print the extracted keywords along with their importance scores.

Conclusion

The WordRank algorithm in Gensim provides a convenient way to extract meaningful keywords from text documents. It combines frequency information with the co-occurrence patterns of words to determine their importance. By using this algorithm, you can automatically identify the most important terms in a document, which can be useful for various NLP tasks such as document summarization, topic modeling, and information retrieval.