Learning to Match Using Local and Distributed Representations of Text for Web Search

Abstract

Models such as latent semantic analysis and those based on neural embeddings learn distributed representations of text, and match the query against the document in the latent semantic space. In traditional information retrieval models, on the other hand, terms have discrete or local representations, and the relevance of a document is determined by the exact matches of query terms in the body text. We hypothesize that matching with distributed representations complements matching with traditional local representations, and that a combination of the two is favorable. We propose a novel document ranking model composed of two separate deep neural networks, one that matches the query and the document using a local representation, and another that matches the query and the document using learned distributed representations. The two networks are jointly trained as part of a single neural network. We show that this combination or `duet' performs significantly better than either neural network individually on a Web page ranking task, and also significantly outperforms traditional baselines and other recently proposed models based on neural networks.

References in corpus (5)

• Convolutional Neural Network Architectures for Matching Natural Language Sentences
• A Deep Relevance Matching Model for Ad-hoc Retrieval
• Using Word Embeddings for Automatic Query Expansion
• Query Expansion with Locally-Trained Word Embeddings
• A Hierarchical Recurrent Encoder-Decoder For Generative Context-Aware Query Suggestion

Cited by in corpus (2)

• Match-Tensor: a Deep Relevance Model for Search
• Luandri: a Clean Lua Interface to the Indri Search Engine