In natural language processing and information retrieval tasks, it is often important to measure the similarity between two documents or texts. One popular similarity measure is the Hellinger distance, which is commonly used when working with probability distributions.

In this blog post, we will see how to calculate the Hellinger similarity using the Gensim library in Python. Gensim is a powerful and easy-to-use Python library for topic modeling, document similarity analysis, and text processing.

Installing Gensim

Before we can start using Gensim, we need to install it. Open your terminal and type the following command:

pip install gensim

Make sure you have pip installed on your machine before running the above command.

Calculating Hellinger Similarity

Once Gensim is installed, we can now calculate Hellinger similarity. Let’s start by importing the necessary libraries:

import gensim
from gensim import corpora, models, similarities

Next, we need to convert our documents into a bag-of-words representation. A bag-of-words model represents each document as a numerical feature vector, where each dimension corresponds to a specific word in the corpus. We can do this using the corpora.Dictionary class in Gensim:

# Example documents
doc1 = "The sky is blue"
doc2 = "The sun is bright"
doc3 = "The cat is sleeping"

# Create a dictionary of all the unique words in the corpus
dictionary = corpora.Dictionary([doc1.split(), doc2.split(), doc3.split()])

# Convert the documents into bag-of-words vectors
corpus = [dictionary.doc2bow(doc.split()) for doc in [doc1, doc2, doc3]]

Once we have our document vectors in the bag-of-words format, we can calculate the Hellinger similarity matrix using the similarities.MatrixSimilarity class:

# Train the TF-IDF model on the corpus
tfidf = models.TfidfModel(corpus)
corpus_tfidf = tfidf[corpus]

# Calculate the similarity matrix
similarity_matrix = similarities.MatrixSimilarity(corpus_tfidf)

# Get the Hellinger similarity between two documents
doc1_idx = 0  # Index of first document
doc2_idx = 1  # Index of second document
hellinger_similarity = similarity_matrix.get_similarities_by_ids(doc1_idx, doc2_idx)

print(f"Hellinger similarity between document {doc1_idx} and document {doc2_idx}: {hellinger_similarity}")

The output will be the Hellinger similarity score between the two specified documents.

Conclusion

In this blog post, we have seen how to calculate Hellinger similarity using the Gensim library in Python. Gensim provides a simple and efficient way to perform document similarity analysis and is widely used in the NLP community.