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研究生:黃奕欽
研究生(外文):I-Chin Huang
論文名稱:利用推論網路加權與反模糊比對之生物醫學文獻詞彙正規化
論文名稱(外文):Normalizing Biomedical Name Entities by Inference Network Weighting and De-ambiguity Matching
指導教授:高宏宇高宏宇引用關係
指導教授(外文):Hung-Yu Kao
學位類別:碩士
校院名稱:國立成功大學
系所名稱:資訊工程學系碩博士班
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2008
畢業學年度:96
語文別:英文
論文頁數:45
中文關鍵詞:詞彙正規化反模糊比對字串比對生物資訊學文字探勘
外文關鍵詞:Inference NetworkText miningBio-informaticsName Entity Normalization
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近年來因為生物醫學文獻的大量增加,相關研究學者無法將文獻知識進行有效的管理與發掘而使得有許多的知識被龐大的文獻所掩埋。為了要建造一個智慧型的生醫知識管理系統,在近幾十年間,學者們提出許多文獻資訊擷取的技術,但是這些技術在擷取資訊之前,系統必須判斷出在文獻中的詞彙,然後再將這些詞彙對應到相關的概念中。在對應的過程中,由於文獻中的詞彙並沒有統一且嚴謹的撰寫格式,所以產生了許多的問題,造成系統沒有辦法透過直接配對將詞彙與概念結合在一起,例如,詞彙的變異,再者,有些詞彙需要透過整篇文獻的了解才能確定其概念,因此,此研究的目的就是要自動且精確地判斷出文獻中有哪些詞彙是學者所提到的相關概念。
此研究透過資訊檢索的技巧,利用推論網路提出加權詞彙相似度的方法,計算出生醫文獻中的詞彙與概念的相似度用以解決詞彙的變異,以及反模糊比對增加文獻中概念的確信度。不同於之前的研究,此研究善加利用了概念與詞彙中的資訊來增加系統的準確性,讓判斷出來的詞彙更具意義。研究結果顯示,系統利用推論網路加權和反模糊比對再配合簡單的詞彙規則所得到的相關概念,能夠在生醫文獻的詞彙正規化中獲得一個顯著的效果。總結而言,此研究希望所提出的方法能夠正規化文獻中的詞彙以協助更高層的相關研究得到更好的效能。
In recent years the number of biomedical literatures is increased dramatically and the related experts cannot efficiently manage and extract knowledge from literatures so that much useful information would be lost. In order to construct an intelligent biomedical knowledge management system, researchers have proposed many Relation Extraction methods during the last several decades. However, before applying those methods the system has to recognize the name entities in literature and map the entity to the relative concept. Due to the less of conscientious and careful writing style, there are many problems, e.g. term variation and term ambiguity, in the mapping process and they cause error correlation between name entity and concept by the directly mapping method. Thus, the purpose of this study is to automatically and exactly identify the relative concepts mentioned in literatures.
In this study, the influence network weighting strategy is applied to weight the similarity score between the entity and the concept as well as to solve the term variation. The proposed de-ambiguity strategy is used to increase the confidence of concept in literature. Different from previous studies, this study makes a good use of the information in entity and concept to increase the precision of system and makes the identified entity even more meaningful. Results of the experiment, the system using those proposed strategies outperforms the simple strategies and previously proposed methods in biomedical entity normalization. Generally, this study proposes to help the next step of text mining researches, e.g. PPI and Co-occurrence, by normalizing the name entity.
中文摘要 IV
ABSTRACT V
1. INTRODUCTION 1
1.1 MOTIVATION 1
1.2 Normalizing Techniques 4
2. RELATED WORK 6
2.1 DATA SOURCE 8
2.1.1 Test Datasets 8
2.1.2 Tagging System 9
2.1.3 Protein/Gene dictionary 10
3. METHOD AND SYSTEM 11
3.1 System Diagram 11
3.2 Pre-processing 13
3.2.1 Building the lexicon 14
3.2.2 Filter Out Stopwords 14
3.2.3 List Domain Common Words 15
3.3 Name Entity Recognition 15
3.4 Normalization Strategy 16
3.4.1 Directly-Match 17
3.4.2 Indirectly-Match 18
3.4.3 Similarity Score 19
3.5 De-ambiguity 24
3.6 Similarity Ranking 26
4. EXPERIMENT AND RESULT 27
4.1 Overview of Experiments 27
4.2 Evaluating strategy 27
4.3 Evaluation of Tagging System 28
4.4 Statistic of data 29
4.5 Threshold Analysis 31
4.5.1 Number of Candidate Dictionary Entities 31
4.5.2 Final Similarity Score 32
4.5.3 Domain Common Word Selection 33
4.6 Evaluation using Public Corpora 35
4.7 Compare with other works 38
4.8 Effective of Tagging System 39
4.9 Efficiency of Select Strategy for Indirectly-Match 40
4.10 Real Curation Experiment 41
5. CONCLUSION AND FEATURE WORK 42
6. REFERENCE 43
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