A Fast Deep Learning Model for Textual Relevance in Biomedical Information Retrieval-Reference-Cited by-同舟云学术

A Fast Deep Learning Model for Textual Relevance in Biomedical Information Retrieval

Published:2018 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18
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Author:

Mohan Sunil¹,Fiorini Nicolas²,Kim Sun²,Lu Zhiyong²

Affiliation:

1. Chan Zuckerberg Initiative, Palo Alto, CA, USA

2. National Center for Biotechnology Information, Bethesda, MD, USA

Funder

Intramural Research Program of the NIH National Library of Medicine

Publisher

ACM Press

Reference10 articles.

1. Olivier Bodenreider. 2004. The Unified Medical Language System (UMLS): integrating biomedical terminology. Vol. 32 (Database issue) (2004), D267--D270.

2. Chris Buckley and Ellen M. Voorhees. 2000. Evaluating Evaluation Measure Stability. In Proceedings of the 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '00). ACM, New York, NY, USA, 33--40.

3. Billy Chiu, Gamal Crichton, Anna Korhonen, and Sampo Pyysalo. 2016. How to Train good Word Embeddings for Biomedical NLP Proceedings of the 15th Workshop on Biomedical Natural Language Processing (BioNLP '16). ACL, 166--174.

4. Fernando Diaz, Bhaskar Mitra, and Nick Craswell. 2016. Query Expansion with Locally-Trained Word Embeddings Proceedings of ACL 2016. ACL, Berlin, Germany, 367--377.

5. Rezarta Islamaj Dogan, G. Craig Murray, Aurélie Névéol, and Zhiyong Lu. 2009. Understanding PubMedtextsuperscript® user search behavior through log analysis. Database Vol. 2009 (2009), bap018.

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