Representing Documents and Queries as Sets of Word Embedded Vectors for Information Retrieval

Abstract

A major difficulty in applying word vector embeddings in IR is in devising an effective and efficient strategy for obtaining representations of compound units of text, such as whole documents, (in comparison to the atomic words), for the purpose of indexing and scoring documents. Instead of striving for a suitable method for obtaining a single vector representation of a large document of text, we rather aim for developing a similarity metric that makes use of the similarities between the individual embedded word vectors in a document and a query. More specifically, we represent a document and a query as sets of word vectors, and use a standard notion of similarity measure between these sets, computed as a function of the similarities between each constituent word pair from these sets. We then make use of this similarity measure in combination with standard IR based similarities for document ranking. The results of our initial experimental investigations shows that our proposed method improves MAP by up to $5.77\%$, in comparison to standard text-based language model similarity, on the TREC ad-hoc dataset.

arXiv commentNeu-IR '16 SIGIR Workshop on Neural Information Retrieval July 21, 2016, Pisa, Italy

References in corpus (1)

• Natural Language Processing (almost) from Scratch

Cited by in corpus (5)

• Semantic Models for the First-stage Retrieval: A Comprehensive Review
• Neural Information Retrieval: A Literature Review
• Extracting Parallel Paragraphs from Common Crawl
• SCDV : Sparse Composite Document Vectors using soft clustering over distributional representations
• Hierarchical Document Encoder for Parallel Corpus Mining