A neural autoencoder approach for document ranking and query refinement in pharmacogenomic information retrieval

Pfeiffer, Jonas ; Broscheit, Samuel ; Gemulla, Rainer ; Göschl, Mathias

A Neural Autoencoder Approach for Document Ranking and Query Refinement in Pharmacogenomic Information Retrieval.pdf - Published

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URL: https://madoc.bib.uni-mannheim.de/45648
Additional URL: http://aclweb.org/anthology/W18-2310
URN: urn:nbn:de:bsz:180-madoc-456486
Document Type: Conference or workshop publication
Year of publication: 2018
Book title: SIGBioMed Workshop on Biomedical Natural Language Processing, Proceedings of the 17th BioNLP Workshop : July 19, 2018 Melbourne, Australia
Page range: 87-97
Conference title: 56th Annual Meeting of the Association for Computational Linguistics
Location of the conference venue: Melbourne, Australia
Date of the conference: 15.-20.7.2018
Publisher: Demner-Fushman, Dina
Place of publication: Stroudsburg, PA
Publishing house: Association for Computational Linguistics
ISBN: 978-1-948087-33-9
Publication language: English
Institution: School of Business Informatics and Mathematics > Practical Computer Science I: Data Analytics (Gemulla 2014-)
License: CC BY 4.0 Creative Commons Attribution 4.0 International (CC BY 4.0)
Subject: 004 Computer science, internet
Abstract: In this study, we investigate learning-to- rank and query refinement approaches for information retrieval in the pharmacogenomic domain. The goal is to improve the information retrieval process of biomedical curators, who manually build knowledge bases for personalized medicine. We study how to exploit the relationships be- tween genes, variants, drugs, diseases and outcomes as features for document ranking and query refinement. For a supervised approach, we are faced with a small amount of annotated data and a large amount of unannotated data. Therefore, we explore ways to use a neural document auto-encoder in a semi-supervised approach. We show that a combination of established algorithms, feature-engineering and a neural auto-encoder model yield promising results in this setting.

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