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研究生:韋絲若
研究生(外文):Dewi Wardani
論文名稱:利用結構化與非結構化特徵改善複雜問答
論文名稱(外文):Finding Structured and Unstructured Features to Improve the Search Result of Complex Question
指導教授:盧文祥盧文祥引用關係
指導教授(外文):Wen-Hsiang Lu
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:2009
畢業學年度:97
語文別:英文
論文頁數:54
外文關鍵詞:question answeringstructured featurecomplex question
相關次數:
  • 被引用被引用:0
  • 點閱點閱:251
  • 評分評分:
  • 下載下載:44
  • 收藏至我的研究室書目清單書目收藏:0
近來,搜索引擎在遭遇到無法有效處理自然語言的問題。有時候,這些問題都是複雜的問題。而一個複雜問題是包含一些句子、意圖或較長答案的問題。

在我們的研究中,我們觀察發現結構特徵和非結構化特徵並使用問題的結構化資料和非結構化資料可以改善複雜問題的搜索結果。根據上面的構想,因此我們結合兩種方法,分別為資訊檢索的方法和QA結構化模板。

我們的研究架構由三部分組成,分別為:問題分析、資源發現和分析相關答案。在問題分析方面,我們提出了一些假設,針對問題試圖找到結構化和非結構化的特徵,結構化特徵指的是結構化資料,而非結構化特徵指的則是非
結構化資料。在資源發現部分,我們結合結構化資料(關連性資料庫)和非結構化資料(網頁)這兩種資料的優點,用以改善取得的相關答案,藉此將能找出網頁本文中最好的區段。在分析相關答案方面,我們進行結構化資料和非結構化資料比對並計算權重分數,最後,我們使用QA模板來重構問題

從實驗的結果得知,尋找結構特徵和非結構化的特徵和使用結構化與非結構化資料,並搭配使用資訊檢索和QA模板可以改善複雜問題的搜索結果。
Recently, search engine got challenge to deal with natural language questions. Sometimes, these questions are complex questions. A complex question is a question that consists of several clauses, several intentions or need long answer.
In this work we proposed that finding structured features and unstructured features of questions and using structured data and unstructured data could improve the search result of complex questions. According to the above ideas, integrate two approaches, IR approach and QA structured template.
Our framework consists of three parts: Question Analysis, Resource Discovery and Analysis of The Relevant Answer. In Question Analysis we used a few assumptions and tried to find structured and unstructured features of the questions. Structured feature tend to Structured data and unstructured feature tend to unstructured data. In the resource discovery we integrated structured data (relational database) and unstructured data (webpage) to take the advantage of two kinds of data to improve and reach the correct answers. We can find the best top fragments from context of the relevant webpages. In the Relevant Answer part and then we made a score matching between the result from structured data and unstructured data, then finally used QA template to reformulate the questions.
For the experimental result, it shows that finding structured feature and unstructured features and using both structured and unstructured data, using approach IR and QA template could improve the search result of complex questions
Abstract I
Abstract II
Table of Content III
List of Figure V
List of Table VI
Chapter 1. Introduction 1
1.1 Motivation 1
1.2 The Considered Problem 3
1.2.1 Question Analysis 3
1.2.2 Resource Discovery and Reach The Relevant Answer 4
1.3 Organization 4
Chapter 2. Related Works 5
2.1 Question Analysis on Question Answering 5
2.1.1 NLP Approach 5
2.1.2 IR Approach 5
2.1.3 Template-based QA 6
2.2 Complex Question 6
2.3 Structured Information to Improve Question Answering 7
2.4 Structured Retrieval 8
2.5 Integration Information 9
Chapter 3. Idea and Method 10
3.1 Observation of Question and Assumptions 10
3.1.1 Complex Question 10
3.1.2 Question from Yahoo!Answer 11
3.2 Idea 12
3.2.1 A Bag of Words of Question Answering Result 12
3.2.2 Finding Answer Using Structured and Unstructured Data 14
3.2.3 Framework 15
3.2.4 A Problem Definitions 16
3.3 Question Analysis 17
3.3.1 A Survey of Question 17
3.3.2 Algorithms and Method of Question Analysis 22
3.4 Resource Discovery 26
3.5 Finding The Relevant Answer 29
Chapter 4. Experiment 33
4.1 Experimental Setup 33
4.1.1 Dataset 33
4.1.2 Experiment Metrics 34
4.2 Experimental Result 35
4.2.1 Question Analysis 35
4.2.2 Resource Discovery and The Relevant Answer 38
Chapter 5. Conclusions and Future Works 50
5.1 Conclusion 50
5.2 Future Works 50
References 52
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