/Search-Solution-using-Word2Vec

In this project, I have tried to build a search solution which can be used by online book publishers and it can aid readers searching for relevant chapters by utilizing the semantic meaning in their query rather than exact matches. It utilizes tools like nltk, gensim and sklearn to build the model and get the top search results.

Primary LanguageJupyter Notebook

Project Overview

In this project, I've developed a search solution tailored for online book publishers to enhance their readers' experience. This solution allows readers to find relevant chapters by understanding the semantic meaning of their queries, rather than relying on exact keyword matches.

Key Features

  • Semantic Search: Leverages the semantic meaning in queries to find the most relevant chapters.
  • Tools and Libraries: Utilizes nltk, gensim, and sklearn to build the search model and retrieve top search results.

Technologies Used

  • NLTK (Natural Language Toolkit): For text preprocessing and tokenization.
  • Gensim: For training Word2Vec models to capture semantic relationships between words.
  • Scikit-learn (sklearn): For calculating similarities and ranking the search results.

This search solution provides a more intuitive and effective way for readers to find content, making it easier to navigate through extensive book collections based on the meaning of their queries.