TAILIEUCHUNG - Báo cáo khoa học: "Semi-Supervised Maximum Entropy Based Approach to Acronym and Abbreviation Normalization in Medical Texts"

Numerous abbreviations are used routinely throughout such texts and identifying their meaning is critical to understanding of the document. The problem is that abbreviations are highly ambiguous with respect to their meaning. For example, according to UMLS 2 (2001), RA may stand for “rheumatoid arthritis”, “renal artery”, “right atrium”, “right atrial”, “refractory anemia”, “radioactive”, “right arm”, “rheumatic arthritis,” etc. Liu et al. (2001) show that 33% of abbreviations listed in UMLS are ambiguous. . | Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics ACL Philadelphia July 2002 pp. 160-167. Semi-Supervised Maximum Entropy Based Approach to Acronym and Abbreviation Normalization in Medical Texts Serguei Pakhomov . Mayo Foundation Rochester MN Abstract Text normalization is an important aspect of successful information retrieval from medical documents such as clinical notes radiology reports and discharge summaries. In the medical domain a significant part of the general problem of text normalization is abbreviation and acronym disambiguation. Numerous abbreviations are used routinely throughout such texts and knowing their meaning is critical to data retrieval from the document. In this paper I will demonstrate a method of automatically generating training data for Maximum Entropy ME modeling of abbreviations and acronyms and will show that using ME modeling is a promising technique for abbreviation and acronym normalization. I report on the results of an experiment involving training a number of ME models used to normalize abbreviations and acronyms on a sample of 10 000 rheumatology notes with 89 accuracy. 1 Introduction and Background Text normalization is an important aspect of successful information retrieval from medical documents such as clinical notes radiology reports and discharge summaries to name a few. In the medical domain a significant part of the general problem of text normalization is abbreviation and acronym1 disambiguation. Numerous abbreviations are used routinely throughout such texts and identifying their meaning is critical to understanding of the document. The problem is that abbreviations are highly ambiguous with respect to their meaning. For example according to UMLS 2 2001 RA may stand for rheumatoid arthritis renal artery right atrium right atrial refractory anemia radioactive right arm rheumatic arthritis etc. Liu et al. 2001 show that 33 of abbreviations listed in .

• Báo cáo khoa học
• Semi Supervised
• Maximum Entropy Based Approach to Acronym
• Abbreviation Normalization in Medical Texts
• báo cáo khoa học
• mô hình ngôn ngữ
• xử lý ngôn ngữ tự nhiên
• Semisupervised condensed nearest neighbor
• part of speech tagging
• Long Papers
• báo cáo ngôn ngữ
• ngôn ngữ tự nhiên
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• Nuclear power plants
• Fault diagnosis scheme
• Graph based SSC algorithm
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• Deep learning
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