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系統識別號 U0026-0502201510420000
論文名稱(中文) 生醫文獻之標註導向辨識與擷取
論文名稱(英文) Curation-oriented Recognition and Retrieval from Biomedical Literature
校院名稱 成功大學
系所名稱(中) 資訊工程學系
系所名稱(英) Institute of Computer Science and Information Engineering
學年度 103
學期 1
出版年 104
研究生(中文) 徐禕佑
研究生(英文) Yi-Yu Hsu
學號 P78971260
學位類別 博士
語文別 英文
論文頁數 97頁
口試委員 指導教授-高宏宇
口試委員-蔣榮先
口試委員-曾新穆
口試委員-謝孫源
口試委員-侯廷偉
口試委員-蔡宗翰
召集委員-林宣華
口試委員-戴鴻傑
中文關鍵字 生醫文獻探勘  生醫名詞同義性  生醫名詞可標定性辨識  生醫文獻標定 
英文關鍵字 Biomedical text mining  Biomedical term synonymity  Curatable biomedical term  Biocuration 
學科別分類
中文摘要 隨著生物醫學文獻的大幅增加,在生醫資料庫整合文獻探勘與機器學習已經成為主流的議題。許多資料庫收集特定主題和對應資源,如實驗數據和研究文獻。但通過文獻探勘處理非結構化數據是一個複雜動態的領域,並且吸引不同學科(如,化學家,生物學家和計算機科學家)的關注。從文獻中自動萃取知識並有效地確認知識記錄在生醫資料庫時,生醫命名實體辨識和文件分流被視為是挑戰。因此在本論文中,我們專注在上述兩議題.
在生醫名詞的同義性辨識部分,如何定義生醫名詞間的語意關係是一個關鍵的議題,有別於過往由生醫文獻標定者提供的語意庫與建立知識本體等方法,我們利用網路搜尋引擎並設計字詞間的評估演算法來決定生醫名詞的同義性,此演算法使用生醫名詞在句子的結構與其對應相容網路資源容器的鏈結關係來評分,其結果能有效解決生醫名詞的同義辨識問題。
在生醫名詞的可標定性辨識部分,由於生醫名詞辨識在發展生醫資料庫時扮演相當重要的角色。然而,現存的生醫名詞辨識工具會產生各式各樣的命名實體,這些命名實體可衍生為可標定或不可標定的生醫名詞。為了提供生醫文獻標定者一個更直觀的標定方法,將生醫名詞分類成可標定或不可標定能有效幫助加速標定流程。它能提供使用者來辨識基因、化合物、疾病、反應作用等出現在比較基因毒理學資料庫的生醫名詞。我們利用條件隨機場域與潛藏狄利克里分配於生醫名詞辨識任務,其結果能有效找出可標定的生醫名詞。
在生醫資料庫文獻探勘的部分,為了有效降低生醫文獻標定者的工作負擔,我們建立一個生醫名詞共頻辨識文獻系統,此系統利用生醫名詞辨識模組來自動偵測生醫文獻中的基因、化合物、疾病等名詞,之後我們將這些生醫名詞依他們的共頻關係與網路鍊結來排序生醫文獻的重要性。這個生醫文獻系統能有效幫助生醫文獻標定者加快標定生醫名詞的速度。在2012國際生醫文獻自動探勘競賽的比較毒物基因組學資料庫文件分類比賽項目,我們的系統在平均準確率上以0.778獲得所有參賽隊伍中第二名的成績。
英文摘要 With a huge increase of biomedical literature, there has been an upsurge need for integrating text mining and machine learning in biological databases. Many databases have collected specific topics and corresponding resources, such as experimental data and research literature. However, processing unstructured data through text mining is a complex and dynamic area, which interests different disciplines (e.g., chemists, biologists, and computer scientists. To automatically extract knowledge from texts and effectively confirm the knowledge recorded in biological databases, the biomedical named-entity recognition (NER) and document triage have been considered as more challenging tasks. Thus, we focus on the two major topics in this dissertation.

Determining the semantic relatedness of two biomedical terms is an important task for many text-mining applications in the biomedical field. Previous studies, such as those using ontology-based and corpus-based approaches, measured semantic relatedness by using information from the structure of biomedical literature, but these methods are limited by the small size of training resources. To increase the size of training datasets, the outputs of search engines have been used extensively to analyze the lexical patterns of biomedical terms. In this work, we propose the Mutually Reinforcing Lexical Pattern Ranking (ReLPR) algorithm for learning and exploring the lexical patterns of synonym pairs in biomedical text. ReLPR employs lexical patterns and their pattern containers to assess the semantic relatedness of biomedical terms. By combining sentence structures and the linking activities between containers and lexical patterns, our algorithm can explore the correlation between two biomedical terms.
NER plays an important role in the development of biological databases. However, the existing NER tools produce multifarious named-entities which may result in both curatable and non-curatable markers. To facilitate biocuration with a straightforward approach, classifying curatable named-entities is helpful with regard to accelerating the biocuration workflow. Co-occurrence Interaction Nexus with Named-entity Recognition (CoINNER) is a web-based tool that allows users to identify genes, chemicals, diseases, and action term mentions in the Comparative Toxicogenomic Database (CTD). We extended our previous system in developing CoINNER. The pre-tagging results of CoINNER were developed based on the state-of-the-art named entity recognition tools in BioCreative III. Next, a method based on conditional random fields (CRFs) is proposed to predict chemical and disease mentions in the articles. Finally, action term mentions were collected by latent Dirichlet allocation (LDA). The results of the CoINNER were significantly superior to those of previous methods.
In recent years, there was a rapid increase in the number of medical articles. The number of articles in PubMed has increased exponentially. Thus, the workload for biocurators has also increased exponentially. Under these circumstances, a system that can automatically determine in advance which article has a higher priority for curation can effectively reduce the workload of biocurators. Determining how to effectively find the articles required by biocurators has become an important task, the Article Classification Task (ACT). In the BioCreative 2012 workshop, we proposed the Co-occurrence Interaction Nexus (CoIN) for learning and exploring relations in articles. We constructed a co-occurrence analysis system, which is applicable to PubMed articles and suitable for gene, chemical and disease queries. CoIN uses co-occurrence features and their network centralities to assess the influence of curatable articles from the Comparative Toxicogenomics Database. The experimental results show that our network-based approach combined with co-occurrence features can effectively classify curatable and non-curatable articles. CoIN also allows biocurators to retrieve the related articles for specific queries without reviewing meaningless information. At the BioCreative CTD ACT Task, CoIN achieved a 0.778 mean average precision in the triage task, thus finishing in second place out of all participants.
論文目次 中文摘要 III
ABSTRACT V
誌謝 VII
TABLE OF CONTENT VIII
LIST OF FIGURE X
LIST OF TABLE XII
CHAPTER 1: INTRODUCTION 1
1.1 OVERVIEW OF THE DISSERTATION 6
1.2 INTRODUCTION OF BIOMEDICAL TERM SYNONYMITY 8
1.3 INTRODUCTION OF CURATABLE BIOMEDICAL TERMS 9
1.4 INTRODUCTION OF RETRIEVAL SYSTEMS FOR BIOCURATION 10
CHAPTER 2: ASSESS THE SEMANTIC RELATEDNESS OF BIOMEDICAL TERMS 13
2.1 INTRODUCTION 13
2.2 PROBLEM STATEMENT 17
2.3 SYSTEM FRAMEWORK OF RELPR 23
2.3.1 Acquisition of Synonym Pairs 24
2.3.2 Crawl Concept Pairs from Search Engines 25
2.3.3 Extracting Lexical Patterns from Snippets 26
2.3.4 ReLPR: Mutually Reinforcing Lexical Pattern Ranking Algorithm 27
2.3.5 Measuring the Semantic Relatedness 30
2.4 EVALUATION OF BIOMEDICAL CONCEPT PAIRS 31
2.5 COMPARISON OF PREVIOUS APPROACHES 39
2.6 SUMMARY 41
CHAPTER 3: CURATABLE BIOMEDICAL TERM RECOGNITION 42
3.1 INTRODUCTION 42
3.2 COINNER ARCHITECTURE 49
3.2.1 A Curatable Sentence Classifier 50
3.2.2 Gene/chemical/disease Named-entity Recognition 55
3.2.3 Action Term Named-entity Recognition 55
3.3 EVALUATION OF THE BIOCREATIVE CTD NER TASK 57
3.4 SUMMARY 63
CHAPTER 4: DOCUMENT TRIAGE SYSTEM FOR BIOMEDICAL LITERATURE 65
4.1 INTRODUCTION 65
4.2 APPROCAHES TO INFORAMTION RETREIVAL IN BIOLOGY 66
4.3 COIN ARCHITERURE 69
4.3.1 Curation Workflow 70
4.3.2 Co-occurrence Models 75
4.3.3 Network-Based Models 75
4.4 EVALUATION OF THE BIOCREATIVE CTD ACT TASK 78
4.5 SUMMARY 85
CHAPTER 5: CONCLUSIONS 87
REFERENCES 90
APPENDIX 96
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