A resource-saving collective approach to biomedical semantic role labeling

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Please use this identifier to cite or link to this item: https://ir.lib.ncu.edu.tw/handle/987654321/106134

Title:	A resource-saving collective approach to biomedical semantic role labeling
Authors:	蔡宗翰;Tsai, Richard Tzong-Han;Lai, Po-Ting
Contributors:	資訊電機學院資訊工程學系
Keywords:	Accuracy;Algorithms;Bioinformatics;Biomedical and Life Sciences;Biomedical Research;Computational Biology/Bioinformatics;Computer Appl. in Life Sciences;Computer science;Data Mining;Databases, Factual;Knowledge-based analysis;Labeling;Life Sciences;Markov Chains;Microarrays;Proteins;Research Article;Semantics;Trees
Date:	2014-05-27
Issue Date:	2026-04-23 13:09:50 (UTC+8)
Publisher:	BioMed Central Ltd.;London: BioMed Central
Abstract:	摘要： Background Biomedical semantic role labeling (BioSRL) is a natural language processing technique that identifies the semantic roles of the words or phrases in sentences describing biological processes and expresses them as predicate-argument structures (PAS’s). Currently, a major problem of BioSRL is that most systems label every node in a full parse tree independently; however, some nodes always exhibit dependency. In general SRL, collective approaches based on the Markov logic network (MLN) model have been successful in dealing with this problem. However, in BioSRL such an approach has not been attempted because it would require more training data to recognize the more specialized and diverse terms found in biomedical literature, increasing training time and computational complexity. Results We first constructed a collective BioSRL system based on MLN. This system, called collective BIOSMILE (CBIOSMILE), is trained on the BioProp corpus. To reduce the resources used in BioSRL training, we employ a tree-pruning filter to remove unlikely nodes from the parse tree and four argument candidate identifiers to retain candidate nodes in the tree. Nodes not recognized by any candidate identifier are discarded. The pruned annotated parse trees are used to train a resource-saving MLN-based system, which is referred to as resource-saving collective BIOSMILE (RCBIOSMILE). Our experimental results show that our proposed CBIOSMILE system outperforms BIOSMILE, which is the top BioSRL system. Furthermore, our proposed RCBIOSMILE maintains the same level of accuracy as CBIOSMILE using 92% less memory and 57% less training time. Conclusions This greatly improved efficiency makes RCBIOSMILE potentially suitable for training on much larger BioSRL corpora over more biomedical domains. Compared to real-world biomedical corpora, BioProp is relatively small, containing only 445 MEDLINE abstracts and 30 event triggers. It is not large enough for practical applications, such as pathway construction. We consider it of primary importance to pursue SRL training on large corpora in the future. 其他題名： BMC Bioinformatics 出版者： London: BioMed Central 出版日期： 2014-05-27 出處： BMC bioinformatics, 2014-05, Vol.15 (1), p.160-160, Article 160 資源來源： Publicly Available Content Database 版權： Tsai and Lai; licensee BioMed Central Ltd. 2014 版權： COPYRIGHT 2014 BioMed Central Ltd. 版權： 2014 Tsai and Lai; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. 版權： Copyright © 2014 Tsai and Lai; licensee BioMed Central Ltd. 2014 Tsai and Lai; licensee BioMed Central Ltd. 識別號： ISSN: 1471-2105 識別號： EISSN: 1471-2105 識別號： DOI: 10.1186/1471-2105-15-160 識別號： PMID: 24884358
Appears in Collections:	[Department of Computer Science and information Engineering] journal & Dissertation

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