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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 翁昭旼(Jau-Min Wong) | |
| dc.contributor.author | Chih-Wei Chen | en |
| dc.contributor.author | 陳志偉 | zh_TW |
| dc.date.accessioned | 2021-06-13T00:09:11Z | - |
| dc.date.available | 2009-07-31 | |
| dc.date.copyright | 2007-07-31 | |
| dc.date.issued | 2007 | |
| dc.date.submitted | 2007-07-26 | |
| dc.identifier.citation | 1. MedLine, http://www.ncbi.nlm.nih.gov/PubMed/.
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Wilbur, Tagging gene and protein names in biomedical text. Bioinformatics, 2002. 18(8): p. 1124-32. 23. Chieu, H.L. and H.T. Ng. Named entity recognition with a maximum entropy approach. in Proceedings of CoNLL-2003. 2003. Edmonton, Canada. 24. Mayfield, J., P. McNamee, and C. Piatko. Named entity recognition using hundreds of thousands of features. in Proceedings of the Seventh Conference on Natural Language Learning (CoNLL-2003). 2003. Edmonton, Canada. 25. Zhou, G. and J. Su. Named entity recognition using an HMM-based chunk tagger in Proceedings of the 40th Annual Meeting on Association for Computational Linguistics 2001 26. Collier, N., C. Nobata, and J.-i. Tsujii. Extracting the Names of Genes and Gene Products with a Hidden Markov Model. in Proceedings of the 18th conference on Computational linguistics 2000. 27. Shen, D., et al., Effective adaptation of a Hidden Markov Model-based named entity recognizer for biomedical domain. 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Search Engine World. http://www.searchengineworld.com/spy/stopwords.htm. 2002. 36. Frakes, W.B. and R. Baeza-Yates, Information retrieval data structure & algorithms. Prentice Hall PTR Released, 1992. 37. Torii, M., S. Kamboj, and K. Vijay-Shanker, An Investigation of Various Information Sources for Classifying Biological Names. Proceedings of the ACL 2003 workshop on Natural language processing in biomedicine, 2003. 13: p. 113 - 120 38. Rindflesch TC, T.L., Weinstein JW, Hunter L. EDGAR: extraction of drugs, genes and relations from the biomedical literature. in Proceedings of the pacific symposium on biocomputing 2000( PSB'2000 ). 2000. 39. Subramaniam, L.V., et al. Information Extraction from Biomedical Literature: Methodology, Evaluation and an Application. in In the Proceedings of the ACM Conference on Information and Knowledge Management. 2003. New Orleans, Lousiana. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/28469 | - |
| dc.description.abstract | 在生醫文獻中的生醫詞彙，存在著例如複合字、同義詞、慣用語、甚至新的命名法則的問題，造成不同文獻中的生醫詞彙未必具有一致性，這使得自動化生醫資料整合的目標因此困難重重。而其中最初步，對系統效能影響最深遠的，莫過於如何從文獻中正確的找出生醫詞彙，即生物醫學名詞辨識( Biomedical Named Entity Recognition, Biomedical NER )。
我們在這篇論文中將利用隱藏式馬可夫模型( Hidden Markov Model )，針對文獻中的摘要部份進行剖析。目標是從文獻摘要中找出生醫詞彙。我們的方法共有四個步驟：首先利用五種生醫詞彙的特徵對文字做分群。第二步，利用分群好的訓練資料產生一個隱藏式馬可夫模型。第三步，將使用者輸入的文章讀入，並且依照前述的四種生醫詞彙特徵對文字做分群。最後，利用Machine Learning演算法，將讀入的文章中，系統判定為生醫詞彙之文字做標記。 | zh_TW |
| dc.description.abstract | With the progress of biomedical science, text mining in biomedical domain is getting important. Since there are many irregularities and ambiguous contexts in biomedical literature such as various compound words, synonyms, acronyms, and even the laws of naming are not literally consistent, how to correctly identify biological terms from text is a fundamental requirement for information extraction.
In this paper we propose a biological term extractor which is based on Hidden Markov Models. There are four steps to accomplish our task. First, the tokens in training data are clustered by five features at the first stage. Second, train a Hidden Markov Model by these clustering tokens. Third, normalize user’s input and cluster these tokens. Finally, annotate the biological terms according to the Machine Learning algorithm. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-13T00:09:11Z (GMT). No. of bitstreams: 1 ntu-96-R94548059-1.pdf: 811285 bytes, checksum: f3a73ac12bc0d6d26dafc70a7269c0b0 (MD5) Previous issue date: 2007 | en |
| dc.description.tableofcontents | 口試委員會審定書
誌謝 1 中文摘要 5 英文摘要 6 第一章 緒論 7 1.1 研究動機與目的 7 1.2 研究材料 9 1.3 研究方法與論文架構 10 第二章 相關工作 11 2.1 使用字典 11 2.2 使用規則資料庫( rule-base )過濾法 12 2.3 使用機器學習 14 第三章 研究方法 15 3.1 系統架構 15 3.2 隱藏式馬可夫模型 16 3.3 系統所使用的特徵集( Feature Set ) 25 3.3.1詞性特徵( Part-of-speech feature ) 27 3.3.2 拼字法特徵( Orthographic features ) 27 3.3.3 形態學特徵( Morphological feature ) 32 3.3.4 詞首特徵( Head noun feature ) 32 3.3.5 形容詞特徵 33 第四章 結果與討論 35 4.1 Genia Corpus 3.02版 36 第五章 結論與未來 39 參考文獻 40 圖表目錄 圖1.1 Genia Corpus之Ontology 10 圖3.1 系統架構圖 15 圖3.2 擲骰子狀態轉移圖 17 圖3.3 Forward變數示意圖 21 表3.1 標點符號標籤說明表 29 表3.2 型態符號標籤說明表 31 表4.1 加入形容詞特徵前後效能比較表 35 表4.2 效能比較表 36 圖4.1 MEDLINE:95280913 36 圖4.2 MEDLINE:95256242 37 表4.3 Genia Corpus分歧紀錄表 37 表4.4 Genia Corpus修改前後系統效能 38 | |
| dc.language.iso | zh-TW | |
| dc.subject | 文字探勘 | zh_TW |
| dc.subject | 隱藏式馬可夫模型 | zh_TW |
| dc.subject | 機器學習 | zh_TW |
| dc.subject | 生醫文獻探勘 | zh_TW |
| dc.subject | 生物醫學名詞辨識 | zh_TW |
| dc.subject | Biomedical Term Extraction | en |
| dc.subject | Text Mining | en |
| dc.subject | Hidden Markov Models | en |
| dc.subject | Machine Learning | en |
| dc.subject | Biomedical Named Entity Recognition | en |
| dc.title | 生醫詞彙辨識：利用隱藏式馬可夫模型 | zh_TW |
| dc.title | Biological Terms Recognition：Using Hidden Markov Models | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 95-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 蔣以仁(I-Jen Chiang),陳中明(Chung-Ming Chen) | |
| dc.subject.keyword | 隱藏式馬可夫模型,機器學習,生醫文獻探勘,生物醫學名詞辨識,文字探勘, | zh_TW |
| dc.subject.keyword | Hidden Markov Models,Machine Learning,Biomedical Term Extraction,Biomedical Named Entity Recognition,Text Mining, | en |
| dc.relation.page | 42 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2007-07-30 | |
| dc.contributor.author-college | 工學院 | zh_TW |
| dc.contributor.author-dept | 醫學工程學研究所 | zh_TW |
| 顯示於系所單位： | 醫學工程學研究所 | |
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