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研究生:蕭微涓
研究生(外文):Wei-Chuan Hsiao
論文名稱:整合主體、類別和屬性識別的知識庫簡單問題問答系統
論文名稱(外文):Integrating Subject, Type, and Property Identification for Simple Question Answering over Knowledge Base
指導教授:陳信希陳信希引用關係
指導教授(外文):Hsin-Hsi Chen
口試日期:2017-07-21
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:資訊工程學研究所
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:2017
畢業學年度:105
語文別:英文
論文頁數:49
中文關鍵詞:問答系統簡單問題知識庫知識三元組雙向長短期記憶模型
外文關鍵詞:Question answering systemsimple questionknowledge basknowledge triplebi-directional long short-term memory model
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隨著網際網路高度普及化,每天都有許多新知識產生。這些新知識經過整理後,以知識庫的形式儲存,如Freebase和DBpedia。有了這些豐富的資源,如何有效率地從中獲取需要的資訊是個很重要的課題。自然語言問答系統是最直接且貼近人們生活的一項應用,使用者可以用熟悉的語言提出任何問題,並透過問答系統從知識庫中獲取答案。
本研究提出一套識別知識庫中主體、類別和屬性的仿真陳述問答系統,以回答簡單類型的問題。我們首先提出數種新的特徵,使得問題中候選主體的排序更加準確。同時,我們也將知識庫中的關係分為類別和屬性,並分別以一個雙向長短期記憶模型進行識別。實驗結果顯示,我們的系統在SimpleQuestions資料集上,達到目前最好的效能。
With the popularity of the Internet, more and more new information is generated every day. The information can be stored in knowledge base, such as Freebase and DBpedia. To access the knowledge efficiently and quickly to acquire what users need, the most direct and close to people''s life is question answering system in natural language. People can ask any questions in their familiar languages, and then use the question answering system to get answers from the knowledge base.
This study presents an approach to identify subject, type and property from knowledge base for answering factoid simple questions. We propose new features to rank entity candidates in knowledge base. Besides, we split a relation in knowledge base into type and property. Each of them is modeled by a bi-directional long short-term memory for identification. Experimental results show that our model achieves the state-of-the-art performance on the SimpleQuestions dataset.
口試委員會審定書 i
誌謝 ii
中文摘要 iii
Abstract iv
Contents v
List of Figures vii
List of Tables viii
Chapter 1 Introduction 1
1.1 Knowledge Base 1
1.2 Question Answering System 2
1.2.1 Introduction 2
1.2.2 Question Classification 2
1.2.3 Challenges in Question Answering System 4
1.3 Motivation 6
1.4 Organization 7
Chapter 2 Related Work 8
2.1 Question Answering Dataset 8
2.1.1 SimpleQuestions 8
2.1.2 WebQuestions 9
2.1.3 ComplexQuestions 10
2.1.4 30M Factoid Question-Answer Corpus 11
2.1.5 WikiMovies 12
2.2 Semantic Parsing Approach 13
2.3 Information Extraction Approach 14
2.4 External Resource Assistance 16
Chapter 3 Methods 19
3.1 Overview 19
3.2 Entity Identification 20
3.2.1 Candidate Generation 20
3.2.2 Feature Calculation 21
3.2.3 Ranking 27
3.3 Type Identification 28
3.4 Property Identification 31
Chapter 4 Experiments 33
4.1 Dataset and Evaluation 33
4.2 Experimental Setup 34
4.3 Overall Result 35
4.4 Entity Identification Result 36
4.5 Importance of Entity Identification Features 37
4.5.1 Performances with Single Feature 37
4.5.2 Performances Without One of the Features 38
4.5.3 Performances Without a Group of Features 39
4.6 Importance of Type Identification 40
4.7 Error Analysis 41
Chapter 5 Conclusion 44
Reference 45
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