政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/54463

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/54463

English | 正體中文 | 简体中文 | Post-Print筆數 : 27 | Items with full text/Total items : 109952/140891 (78%)
Visitors : 46240077 Online Users : 1019

RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.

Scope

please add "double quotation mark" for query phrases to get precise results

please goto advance search for comprehansive author search

Adv. Search

Home ‧ Login ‧ Upload ‧ Help ‧ About ‧ Administer

Goto mobile version

政大典藏 > College of Informatics > Department of Computer Science > Theses > Item 140.119/54463

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/54463

Title:	利用馬可夫邏輯網路模型與自動化生成的模板加強生醫文獻之語意角色標註 Biomedical semantic role labeling with a Markov Logic network and automatically generated patterns
Authors:	賴柏廷
Contributors:	蔡宗翰劉昭麟 Tsai, Richard Tzong Han Liu, Chao Lin 賴柏廷
Keywords:	語意角色標註自然語言處理馬可夫邏輯網路機器學習資訊擷取 Semantic Role Labeling Natural Language Processing Markov Logic Network Machine Learning Information Extraction
Date:	2011
Issue Date:	2012-10-30 11:07:49 (UTC+8)
Abstract:	背景: 生醫文獻語意角色標註（Semantic Role Labeling, SRL）是一種自然語言處理的技術，其可用來將描述生物過程的語句以predicate-argument structures ( PASs ) 表示。SRL 經常受限於arguments的unbalance problem而且需要花費許多的時間和記憶體空間在學習 arguments 之間的相依性。方法: 我們提出一Markov Logic Network ( MLN ) -based SRL之系統，且此系統使用自動化生成之SRL 模板同時辨識constituents與候選之語意角色。結果及結論: 我們的方法在BioProp語料上來評估。實驗結果顯示我們的方法勝過目前最先進的系統。此外，使用SRL模板後，在時間及記憶體之花費上亦大幅的減少，而且我們自動化生成之模板亦能幫助建立這些模板。我們認為本論文提出之方法可以透過增加新的SRL模板例如：由生物學家手動寫的模板，而得到進一步的提升，而且本方法也為於需要處理大量SRL 語料時，提供一種可能的解法。 Background: Biomedical semantic role labeling ( SRL ) is a natural language processing technique that expresses the sentences that describe biological processes as predicate-argument structures ( PASs ) . SRL usually suffers from the unbalanced problem of arguments and consuming time and memory on learning the dependencies between the arguments. Method: We constructed a Markov Logic Network ( MLN ) -based SRL system, and the system uses SRL patterns, which utilizes automatically generated approaches, to simultaneously recognize the constituents and candidates of semantic roles. Results and conclusions: Our method is evaluated on the BioProp corpus. The experimental result shows that our method outperforms the state-of-the-art system. Furthermore, after applying SRL patterns, the costs of the time and memory are greatly reduced, and our automatically generated patterns are helpful in the development of these patterns. We consider that our method can be further improved by adding new SRL patterns such as biological experts manually written patterns and it also provide a possible solution to process large SRL corpus.
Reference:	[1] H.-J. Dai, Y.-C. Chang, R. Tzong-Han Tsai, and W.-L. Hsu, "New Challenges for Biological Text-Mining in the Next Decade," Journal of Computer Science and Technology, vol. 25, pp. 169-179, 2010. [2] R. T.-H. Tsai, W.-C. Chou, Y.-S. Su, Y.-C. Lin, C.-L. Sung, H.-J. Dai, I. T.-H. Yeh, W. Ku, T.-Y. Sung, and W.-L. Hsu, "BIOSMILE: a semantic role labeling system for biomedical verbs using a maximum-entropy model with automatically generated template features," BMC Bioinformatics, vol. 8, p. 325, 2007. [3] S. Pradhan, W. Ward, K. Hacioglu, J. Martin, and D. Jurafsky, "Shallow Semantic Parsing using Support Vector Machines," in Proceedings of the Human Language Technology Conference/North American chapter of the Association for Computational Linguistics annual meeting (HLT/NAACL-2004), Boston, MA, USA, 2004. [4] K. B. Cohen and L. Hunter, "A critical review of PASBio`s argument structures for biomedical verbs.," BMC Bioinformatics, vol. 7, 2006. [5] S. Pradhan, K. Hacioglu, V. Krugler, W. Ward, J. H. Martin, and D. Jurafsky. (2005). Support Vector Learning for Semantic Argument Classification [6] T. Cohn and P. Blunsom, "Semantic role labelling with tree conditional random fields," in In Proceedings of CoNLL-2005, ed, 2005, pp. 169-172. [7] P. Kingsbury and M. Palmer, "From Treebank to PropBank," ed, 2002. [8] X. Carreras and L. Marquez, "Introduction to the CoNLL-2005 Shared Task: Semantic Role Labeling," 2005. [9] D. Gildea and M. Palmer, "The necessity of parsing for predicate argument recognition," in ACL `02: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, 2001, pp. 239-246. [10] V. Punyakanok, D. Roth, W.-t. Yih, and D. Zimak, "Semantic role labeling via integer linear programming inference," in In Proceedings of COLING-04, ed, 2004, pp. 1346-1352. [11] S. Riedel, "Improving the accuracy and Efficiency of MAP Inference for Markov Logic," in Proceedings of the 24th Annual Conference on Uncertainty in AI (UAI `08), ed, 2008, pp. 468-475. [12] P. Domingos and M. Richardson, "Markov Logic: A Unifying Framework for Statistical Relational Learning," in PROCEEDINGS OF THE ICML-2004 WORKSHOP ON STATISTICAL RELATIONAL LEARNING AND ITS CONNECTIONS TO OTHER FIELDS, 2004, pp. 49-54. [13] M. Richardson and P. Domingos, "Markov logic networks," Machine Learning, vol. 62, pp. 107-136, 2006. [14] K. Crammer and Y. Singer, "Ultraconservative online algorithms for multiclass problems," Journal of Machine Learning Research, vol. 3, pp. 951-991, 2003. [15] S. Riedel, "Improving the accuracy and efficiency of map inference for markov logic," presented at the Proceedings of UAI 2008, 2008. [16] D. Gildea and D. Jurafsky, "Automatic labeling of semantic roles," Comput. Linguist., vol. 28, pp. 245-288, 2002. [17] N. Xue, "Calibrating features for semantic role labeling," in In Proceedings of EMNLP 2004, ed, 2004, pp. 88-94. [18] M. Surdeanu, S. Harabagiu, J. Williams, and P. Aarseth, "Using predicate-argument structures for information extraction," presented at the Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1, Sapporo, Japan, 2003. [19] R. Agrawal, T. Imieli\\, \\#324, ski, and A. Swami, "Mining association rules between sets of items in large databases," SIGMOD Rec., vol. 22, pp. 207-216, 1993. [20] S. Riedel and I. Meza-Ruiz, "Collective semantic role labelling with Markov logic," presented at the Proceedings of the Twelfth Conference on Computational Natural Language Learning, Manchester, United Kingdom, 2008. [21] T. Wattarujeekrit, P. Shah, and N. Collier, "PASBio: predicate-argument structures for event extraction in molecular biology," BMC Bioinformatics, vol. 5, p. 155, 2004. [22] P. Thompson, S. Iqbal, J. McNaught, and S. Ananiadou, "Construction of an annotated corpus to support biomedical information extraction," BMC Bioinformatics, vol. 10, p. 349, 2009. [23] A. Bies, "Bracketing Guidelines for Treebank II Style Penn Treebank Project," ed, 1995. [24] W.-C. Chou, R. T.-H. Tsai, Y.-S. Su, W. Ku, T.-Y. Sung, and W.-L. Hsu, "A semi-automatic method for annotating a biomedical proposition bank," presented at the Proceedings of the Workshop on Frontiers in Linguistically Annotated Corpora 2006, Sydney, Australia, 2006. [25] J. D. Kim, T. Ohta, Y. Tateisi, and J. Tsujii, "GENIA corpus -- a semantically annotated corpus for bio-textmining," Bioinformatics, vol. 19, pp. i180-i182, 2003. [26] D. Dahlmeier and H. T. Ng, "Domain Adaptation for Semantic Role Labeling in the Biomedical Domain," Bioinformatics (Oxford, England), 2010. [27] S. Bethard, Z. Lu, J. Martin, and L. Hunter, "Semantic Role Labeling for Protein Transport Predicates," BMC Bioinformatics, vol. 9, p. 277, 2008.
Description:	碩士國立政治大學資訊科學學系 99753004 100
Source URI:	http://thesis.lib.nccu.edu.tw/record/#G0099753004
Data Type:	thesis
Appears in Collections:	[Department of Computer Science ] Theses

Files in This Item:

File	Size	Format
300401.pdf	1406Kb	Adobe PDF2	555	View/Open

All items in 政大典藏 are protected by copyright, with all rights reserved.

社群 sharing

著作權政策宣告 Copyright Announcement

1.本網站之數位內容為國立政治大學所收錄之機構典藏，無償提供學術研究與公眾教育等公益性使用，惟仍請適度，合理使用本網站之內容，以尊重著作權人之權益。商業上之利用，則請先取得著作權人之授權。
The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

2.本網站之製作，已盡力防止侵害著作權人之權益，如仍發現本網站之數位內容有侵害著作權人權益情事者，請權利人通知本網站維護人員(nccur@nccu.edu.tw)，維護人員將立即採取移除該數位著作等補救措施。
NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.

DSpace Software Copyright © 2002-2004 MIT & Hewlett-Packard / Enhanced by NTU Library IR team Copyright © - Feedback