Query Expansion Using Word Embeddings

2016/10/24 by Saar Kuzi, Anna Shtok, Oren Kurland · 5 citations
Computer Science · #Information Retrieval and Search Behavior #Topic Modeling #Web Data Mining and Analysis

paper · doi:10.1145/2983323.2983876

openalex publication_date 2016/10/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29

Abstract

We present a suite of query expansion methods that are based on word embeddings. Using Word2Vec's CBOW embedding approach, applied over the entire corpus on which search is performed, we select terms that are semantically related to the query. Our methods either use the terms to expand the original query or integrate them with the effective pseudo-feedback-based relevance model. In the former case, retrieval performance is significantly better than that of using only the query, and in the latter case the performance is significantly better than that of the relevance model.

Citations

• Using Word Embeddings for Automatic Query Expansion

Cited by

• Learning Representations and Agents for Information Retrieval
• Event-Driven Query Expansion
• Relevance-based Word Embedding
• Query Expansion for Cross-Language Question Re-Ranking
• Query expansion [wikipedia]

Related

• Query Expansion with Locally-Trained Word Embeddings
• Using Word Embeddings for Automatic Query Expansion
• Deep Neural Networks for Query Expansion using Word Embeddings
• Relevance-based Word Embedding
• Query Expansion by Prompting Large Language Models