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A Neural Autoencoder Approach for Document Ranking and Query Refinement in Pharmacogenomic Information Retrieval

Pfeiffer, Jonas and Broscheit, Samuel and Gemulla, Rainer and Göschl, Mathias (2018):
A Neural Autoencoder Approach for Document Ranking and Query Refinement in Pharmacogenomic Information Retrieval.
Melbourne, Australia, Association for Computational Linguistics, In: Proceedings of the BioNLP 2018 workshop, Melbourne, Australia, [Online-Edition: https://www.aclweb.org/anthology/W18-2310],
[Conference or Workshop Item]

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 between 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.

Item Type: Conference or Workshop Item
Erschienen: 2018
Creators: Pfeiffer, Jonas and Broscheit, Samuel and Gemulla, Rainer and Göschl, Mathias
Title: A Neural Autoencoder Approach for Document Ranking and Query Refinement in Pharmacogenomic Information Retrieval
Language: English
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 between 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.

Place of Publication: Melbourne, Australia
Publisher: Association for Computational Linguistics
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Ubiquitous Knowledge Processing
Event Title: Proceedings of the BioNLP 2018 workshop
Event Location: Melbourne, Australia
Date Deposited: 14 Jun 2019 07:05
Official URL: https://www.aclweb.org/anthology/W18-2310
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