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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 孫雅麗(Yeali S. Sun) | |
| dc.contributor.author | Yi-Ting Lin | en |
| dc.contributor.author | 林意婷 | zh_TW |
| dc.date.accessioned | 2021-06-15T02:31:32Z | - |
| dc.date.available | 2009-08-19 | |
| dc.date.copyright | 2009-08-19 | |
| dc.date.issued | 2009 | |
| dc.date.submitted | 2009-08-14 | |
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A. “Computer-Aided Generation of Multiple-Choice Tests.” Proceedings of HLT-NAACL '03 Workshop: Building Educational Applications Using Natural Language Processing (Edmonton, Canada, May 2003), 17-22. 2003. [50] Sumita, E., Sugaya, F., and Yamamoto, S. “Measuring Non-native Speakers’ Proficiency of English by Using a Test with Automatically-Generated Fill-in-the-Blank Questions.” Proceedings of ACL '05 Workshop: Building Educational Applications Using Natural Language Processing (Ann Arbor, USA, June 2005), 61-68. 2005. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/43882 | - |
| dc.description.abstract | 整篇文章是透過名詞片語在貫穿整體文章的連貫性(coherence)，當然電腦或聽者(hearer)要理解文章真正的文意，須透過代名詞與先行詞之間和名詞片語之間的確定動作才可以來完成。例如，在討論台灣、中國、美國、日本等貿易的文章中，語段(discourse)中可能會寫「中華民國」，後面可能會說「台灣」、「中華台北」等，還會提到「這個國家」、「她」等。這些表達方式都是現實世界中「中華民國」的不同表示方式，事實上，它們是指向同一實體。雖然人們可以毫無困難地區分文章中同一實體的不同表現方式，但對電腦來說，仍是非常困難的。在某種意義上來說，共同指向(coreference)在自然語言中存在著超鏈結的作用。一方面，它使得作者在撰寫文章時可以實現文章的連貫性。
在此我們利用共同指向決策方案來完成用以評估學習者文章理解的自動出題。我們將共同指向應用在自動出題上。利用共同指向對文章的關係處理，進而完成文章理解。在此我們使用三種出題型態:代名詞、虛擬代名詞和非代名詞。為了提高題目的困難度和區別力，我們使用共同指向完成的鏈結關係來產生正確和錯誤的選項。學習者需透過名詞片語共同指向的連結來完成文章的理解。在此我們使用策略來完成選項處理。正確選項是取出共同指向鏈結中離目標字最近的名詞片語為答案。而錯誤選項需要與目標字處在不同的共同鏈，但為了增進文章困難度和區別力，錯誤選項的一致性特徵需盡可能與目標字一致(例如:單複數、性別等等)，用以混淆學習者。 | zh_TW |
| dc.description.abstract | In this paper, we propose a multiple-choice question generation program based on coreference resolution for measuring learners’ comprehension of the article. The coreference of the entire article is accomplished by the connection of noun phrases referring to the same entity in the real world. We apply the coreference resolution to the issue of automatic question generation.
Here we have three types of target key: pronoun, pleonastic pronoun, and NP. In order to improve question difficulty and discrimination, we employ clusters’ relation of the coreference to generate the answer and distractor. If readers understand the article, they should know which noun phrases refer to the same entity in the real world. We generate the answers to the questions that are the closest NP of target words in a coreference chain. For discriminating non-proficiency readers form proficiency readers, the answer and distractor of the question are in the similar agreement features (e.g., Number, gender et al) to confuse readers. We generate the distractors of the questions occurring in coreference chains but the one target word occurs in are as similar as possible to the target word in the agreement features. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-15T02:31:32Z (GMT). No. of bitstreams: 1 ntu-98-R96725035-1.pdf: 2830014 bytes, checksum: 500b244340cb39a6df4cfad939ccc903 (MD5) Previous issue date: 2009 | en |
| dc.description.tableofcontents | Table of Contents
論文摘要 IV THESIS ABSTRACT V Table of Contents VI Chapter 1 Introduction 1 1.1 Background 1 1.1.1 Computer Processing of Language 1 1.1.2 Co-reference vs. Anaphora 3 1.1.3 Research Domain of Coreference Resolution 8 1.2 Motivation and Objective 10 1.3 Problem Definition 12 1.4 Thesis Structure 14 Chapter 2 Related Work 15 2.1 Quiz Generation 16 2.1.1 Computer-Aided Generation of Multiple-Choice Tests 16 2.1.2 The Design of Automatic Quiz Generation for Ubiquitous English E-Learning System 17 2.1.3 FAST – An Automatic Generation System for Grammar Tests 20 2.2 Pronoun Resolution 22 2.2.1 Hobbs Algorithm 22 2.2.2 Centering Theory Algorithm 23 2.2.3 RAP (Resolution of Anaphora Procedure) Algorithm 24 2.3 Co-reference Resolution on Machine Learning Algorithm 25 2.3.1 Classification Algorithm 25 2.3.2 Clustering Algorithm 27 2.4 Co-reference Resolution with knowledge 28 2.4.1 Entire Optimization 28 2.4.2 Deep linguistics knowledge and background knowledge 30 2.4.3 Integration of linguistics model and probability learning model 31 Chapter 3 The Proposed Methodology 32 3.1 Pronoun and Non-pronoun Filters 33 3.2 Feature Selection 36 3.2.1 Appositive Feature 37 3.2.2 Equal Pattern Filter Feature 37 3.2.3 Number Agreement Feature 37 3.2.4 Animacy and Person Agreement Feature 38 3.2.5 Gender Agreement Feature 39 3.2.6 Semantic Class Feature 40 3.2.7 Exact String Match Feature 40 3.2.8 First token match (for organization or person) Feature 40 3.2.9 Prepositional phrases (for organization or person) Feature 40 3.2.10 Pleonastic Pronoun Detection Feature 41 3.2.11 Syntactic Filter on Pronoun-NP Coreference Feature 42 3.2.12 Lexical Anaphor Filter Feature 43 3.3 Non-Pronoun Resolution 44 3.4 Pronoun Resolution 45 3.5 Question Generation Method 48 3.5.1 Blank Target Type 49 3.5.2 Determine the answer 49 3.5.3 Collect the distracters 49 Chapter 4 Experiment 51 Chapter 5 Conclusion and Future Work 60 Reference 61 | |
| dc.language.iso | en | |
| dc.title | 以共同指向決策方案的自動多選題產生系統 | zh_TW |
| dc.title | Automatic Multiple-Choice Question Generation based on Coreference Resolution | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 97-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.coadvisor | 陳孟彰(Meng Chang Chen) | |
| dc.contributor.oralexamcommittee | 陳建錦(Chien Chin Chen),張嘉惠(Chia-Hui Chang) | |
| dc.subject.keyword | 連貫性,共同指向,自動出題,虛擬代名詞,非代名詞,共同指向鏈,一致性特徵, | zh_TW |
| dc.subject.keyword | multiple-choice question generation, coreference resolution,automatic question,pleonastic pronoun,NP,distractor,target words,agreement features, | en |
| dc.relation.page | 65 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2009-08-17 | |
| dc.contributor.author-college | 管理學院 | zh_TW |
| dc.contributor.author-dept | 資訊管理學研究所 | zh_TW |
| 顯示於系所單位： | 資訊管理學系 | |
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