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

Jonas Pfeiffer, Samuel Broscheit, Rainer Gemulla, Mathias Göschl

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.
Anthology ID:
W18-2310
Volume:
Proceedings of the BioNLP 2018 workshop
Month:
July
Year:
2018
Address:
Melbourne, Australia
Venue:
BioNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
87–97
Language:
URL:
https://aclanthology.org/W18-2310
DOI:
10.18653/v1/W18-2310
Bibkey:
Cite (ACL):
Jonas Pfeiffer, Samuel Broscheit, Rainer Gemulla, and Mathias Göschl. 2018. A Neural Autoencoder Approach for Document Ranking and Query Refinement in Pharmacogenomic Information Retrieval. In Proceedings of the BioNLP 2018 workshop, pages 87–97, Melbourne, Australia. Association for Computational Linguistics.
Cite (Informal):
A Neural Autoencoder Approach for Document Ranking and Query Refinement in Pharmacogenomic Information Retrieval (Pfeiffer et al., BioNLP 2018)
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https://preview.aclanthology.org/ingestion-script-update/W18-2310.pdf
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