Affiliation:
1. University of Amsterdam, The Netherlands
Abstract
We propose the Neural Vector Space Model (NVSM), a method that learns representations of documents in an unsupervised manner for news article retrieval. In the NVSM paradigm, we learn low-dimensional representations of words and documents from scratch using gradient descent and rank documents according to their similarity with query representations that are composed from word representations. We show that NVSM performs better at document ranking than existing latent semantic vector space methods. The addition of NVSM to a mixture of lexical language models and a state-of-the-art baseline vector space model yields a statistically significant increase in retrieval effectiveness. Consequently, NVSM adds a complementary relevance signal. Next to semantic matching, we find that NVSM performs well in cases where lexical matching is needed. NVSM learns a notion of term specificity directly from the document collection without feature engineering. We also show that NVSM learns regularities related to Luhn significance. Finally, we give advice on how to deploy NVSM in situations where model selection (e.g., cross-validation) is infeasible. We find that an unsupervised ensemble of multiple models trained with different hyperparameter values performs better than a single cross-validated model. Therefore, NVSM can safely be used for ranking documents without supervised relevance judgments.
Funder
Ahold Delhaize, Amsterdam Data Science, the Bloomberg Research Grant program
Criteo Faculty Research Award program, Elsevier
Google Faculty Research Award scheme, the Microsoft Research Ph.D. program, the Netherlands Institute for Sound and Vision, the Netherlands Organisation for Scientific Research
Yandex
European Community's Seventh Framework Programme
Publisher
Association for Computing Machinery (ACM)
Subject
Computer Science Applications,General Business, Management and Accounting,Information Systems
Reference87 articles.
1. Analysis of the Paragraph Vector Model for Information Retrieval
2. Qingyao Ai Liu Yang Jiafeng Guo and W. Bruce Croft. 2016b. Improving language estimation with the paragraph vector model for ad-hoc retrieval. In SIGIR. ACM 869--872. 10.1145/2911451.2914688 Qingyao Ai Liu Yang Jiafeng Guo and W. Bruce Croft. 2016b. Improving language estimation with the paragraph vector model for ad-hoc retrieval. In SIGIR. ACM 869--872. 10.1145/2911451.2914688
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