2018
EMNLP
EMNLP 2018
Semantic role labeling tools for biomedical question answering: a study of selected tools on the BioASQ datasets
Abstract
AbstractQuestion answering (QA) systems usually rely on advanced natural language processing components to precisely understand the questions and extract the answers. Semantic role labeling (SRL) is known to boost performance for QA, but its use for biomedical texts has not yet been fully studied. We analyzed the performance of three SRL tools (BioKIT, BIOSMILE and PathLSTM) on 1776 questions from the BioASQ challenge. We compared the systems regarding the coverage of the questions and snippets, as well as based on pre-defined criteria, such as easiness of installation, supported formats and usability. Finally, we integrated two of the tools in a simple QA system to further evaluate their performance over the official BioASQ test sets.
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Topic Pioneer
— Semantic Role Labeling
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Interdisciplinary Bridge
— Healthcare & Medicine and Natural Language Processing
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Hot Topic Early Bird
— biomedical text
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio