Ontology-Based Retrieval & Neural Approaches for BioASQ Ideal Answer Generation
Ashwin Naresh Kumar, Harini Kesavamoorthy, Madhura Das, Pramati Kalwad, Khyathi Chandu, Teruko Mitamura, Eric Nyberg
Abstract
The ever-increasing magnitude of biomedical information sources makes it difficult and time-consuming for a human researcher to find the most relevant documents and pinpointed answers for a specific question or topic when using only a traditional search engine. Biomedical Question Answering systems automatically identify the most relevant documents and pinpointed answers, given an information need expressed as a natural language question. Generating a non-redundant, human-readable summary that satisfies the information need of a given biomedical question is the focus of the Ideal Answer Generation task, part of the BioASQ challenge. This paper presents a system for ideal answer generation (using ontology-based retrieval and a neural learning-to-rank approach, combined with extractive and abstractive summarization techniques) which achieved the highest ROUGE score of 0.659 on the BioASQ 5b batch 2 test.- Anthology ID:
- W18-5310
- Volume:
- Proceedings of the 6th BioASQ Workshop A challenge on large-scale biomedical semantic indexing and question answering
- Month:
- November
- Year:
- 2018
- Address:
- Brussels, Belgium
- Venue:
- BioASQ
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 79–89
- Language:
- URL:
- https://aclanthology.org/W18-5310
- DOI:
- 10.18653/v1/W18-5310
- Cite (ACL):
- Ashwin Naresh Kumar, Harini Kesavamoorthy, Madhura Das, Pramati Kalwad, Khyathi Chandu, Teruko Mitamura, and Eric Nyberg. 2018. Ontology-Based Retrieval & Neural Approaches for BioASQ Ideal Answer Generation. In Proceedings of the 6th BioASQ Workshop A challenge on large-scale biomedical semantic indexing and question answering, pages 79–89, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- Ontology-Based Retrieval & Neural Approaches for BioASQ Ideal Answer Generation (Naresh Kumar et al., BioASQ 2018)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/W18-5310.pdf