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研究生:謝泓廷
研究生(外文):Hsieh, Hung-Ting
論文名稱:學習於網路擷取專有名詞的別名
論文名稱(外文):Learning to Extract Aliases of Named Entities on the Web
指導教授:張俊盛張俊盛引用關係
指導教授(外文):Chang, Jason S.
學位類別:碩士
校院名稱:國立清華大學
系所名稱:資訊系統與應用研究所
學門:電算機學門
學類:系統設計學類
論文種類:學術論文
論文出版年:2013
畢業學年度:101
語文別:英文
論文頁數:60
中文關鍵詞:關係抽取別名辭典專有名詞網路語料庫條件隨機域
外文關鍵詞:Relation ExtractionAlias LexiconNamed EntityWeb as CorpusConditional Random Field
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在文件中,很多別名可能指得是同一個專有名詞。由於別名在文件中很常見,辨認出專有名詞的別名的功能對於很多應用領域很重要,例如搜尋引擎(像是Google和Bing)或是智慧型對話系統(例如Siri)。在本篇論文中,我們提出一個自動從網路上找尋專有名詞別名的方法。我們的方法在訓練階段會自動產生可用的詞彙樣式來引導搜尋引擎回傳相關含有給定專有名詞之別名的片段。此外,我們將判斷給定專有名詞之別名的邊界視為一個序列標記的問題,並訓練一個機器學習的模組。在執行階段,我們首先藉由擴展給定專有名詞為一組新的查詢來引導搜尋引擎回傳含有相關別名的片段,然後利用訓練好的序列標記模組來判斷別名的邊界。最後我們提出一個系統雛形(AliasFinder),它運用了上述的方法從網路上獲取別名。實驗結果顯示,提出的方法的表現顯著地優於基礎實驗(baseline)。總而言之,本研究提出一個有效找到給定專有名詞的別名的方法,並且被應用於很多專業領域。
A named entity (NE) can be referred to using many aliases in documents. Due to the prevalence of aliases, recognizing aliases of NE becomes an essential part for many applications such as Search Engine (e.g., Google, Bing) and Intelligent Dialog System (e.g., Siri). In this paper, we propose an approach for learning to extract aliases for a given NE on the Web automatically. The method involves generating applicable lexical patterns automatically so as to bias the search engine to return relevance documents containing aliases. Furthermore, we treat the process of identifying boundaries of aliases for a given NE as a sequence labeling problem and train a machine-learning model. At run-time, we bias the search engine to retrieve relevance snippets by transforming the given NE into a set of queries and then identify the boundaries of aliases with the trained model. We present a prototype, AliasFinder, which applies the method to find aliases from the Web. Experimental results show that the proposed method yields better performance than the baselines, provides an efficient way to find aliases of NEs.
摘要 i
Abstract ii
致謝辭 iii
List of Figures v
List of Tables vi
CHAPTER 1 INTRODUCTION 1
CHAPTER 2 RELATED WORKS 5
CHAPTER 3 METHOD 10
3.1 Problem Statement 10
3.2 Collect Training Data 12
3.3 Generate Lexical Patterns 13
3.4 Learn Alias Extraction 16
3.5 Run-Time Stage 23
CHAPTER 4 EXPERIMENTAL SETTING 26
4.1 Training AliasFinder 26
4.2 Systems Compared 28
4.3 Evaluation Metrics 30
4.4 Evaluation NEs and Relevance Judgments 32
CHAPTER 5 EVALUATION RESULTS 36
5.1 Hit Rate and Coverage 36
5.2 Mean Reciprocal Rank (MRR) 39
5.3 Influence of Training Sizes 40
5.4 Compare against Search Engines 42
CHAPTER 6 FUTURE WORKS AND SUMMARY 45

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