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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳信希(Hsin-Hsi Chen) | |
| dc.contributor.author | Ming-Shun Lin | en |
| dc.contributor.author | 林敏順 | zh_TW |
| dc.date.accessioned | 2021-06-15T02:31:34Z | - |
| dc.date.available | 2009-08-18 | |
| dc.date.copyright | 2009-08-18 | |
| dc.date.issued | 2009 | |
| dc.date.submitted | 2009-08-15 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/43883 | - |
| dc.description.abstract | 在自然語言處理中，做統計計算時資源是最重要的，現今有很多現成的語料及被驗証過的語言模型幾乎是隨手可得，而以語料庫為基礎的各種不同的研究中，總是會面臨到語料庫是否可以反應出最新詞彙相關性意義的麻煩。因為語言是活的，新字以及新詞彙每天都會被創造出來，如何知道新字以及新詞彙的相關性意義是一個非常重要的研究議題。
在這篇論文中我們定義了一個新穎的以網頁為基礎的相關性量測方法，並且把網頁當成是一種語料，而我們也會探討不同的網頁領域上對這個方法所帶來的影響。兩個詞彙的關係分數會根據這兩個詞的網頁內容以及詞彙的頻率資訊來加以獲得，兩個詞彙真正的相關性分數是把他們的關係分數透過一個轉移函式的計算而得到的，本論文中一共提出四種轉移函式，分別為卜瓦松函式、對數凸函式、冪凸函式以及岡帕氏函式，在實驗中我們分別使用三個有名的測試集來測試這四個模型，並詳細的與各研究團隊做比較。 在以往的研究中人名一直是非重要的語料資源，我們會利用關係分數來判斷兩個人名是否有關係，在這個關係的辨識中我們提出三個策略：分別為直接關聯法、關聯矩陣法以及純量關聯矩陣法來驗證我們的相關性量測是合理的。我們會利用上述的相關性量測方法去建立一個社群網路，並對這個社群網路的每個配對利用馬可夫隨機程序去標記他們的類別，而且會試著從網頁中抽出關鍵詞當成他們的關係。 在論文中我們也利用我們的相關性量測做查詢詞的推薦，這個查詢詞的推薦與傳統的查詢詞推薦不同，傳統的查詢詞推薦是根據被查詢的記錄檔，我們的查詢詞推薦是從網頁中直接抽取出來。在實驗中我們所提出的方法證明有高度的認同值。 | zh_TW |
| dc.description.abstract | In statistical natural language processing, resources used to compute the statistics are indispensable. Different kinds of corpora have made available and many language models have been experimented. One major issue behind the corpus-based approaches is: if corpora adopted can reflect the up-to-date usage. As we know, languages are live. New terms and phrases are used in daily life. How to capture the new usages is an important research topic.
This thesis defines a novel web-based relatedness measure and explores snippets in various web domains as corpora. Mutual dependency score between two objects is calculated according to content information and frequent information of the two objects. The relatedness score of the two objects is defined as projecting the dependency score by a transfer function. Four transfer functions based on Poisson, Log-concave Power-concave and Gompertz function are considered. Three famous benchmark datasets, including WordSimilarity-353, Miller-Charles and Rubenstein-Goodenough, verify the four transfer functions. Named entities are common foci of searchers. We apply the dependency score to evaluate named level association by three strategies, direct association, association matrix and scalar association matrix. Modeling and naming general entity-entity relationships is challenging in construction of social networks. Given a seed denoting a person name, we utilize Google search engine, NER (Named Entity Recognizer) parser, and the web-based relatedness measure to construct an evolving social network. For each entity pair in the network, we apply Markov chain random process to extract potential categories defined in the ODP. Moreover, for labeling their relationships, we try to combine the tf×idf scores of noun phrases extracted from snippets and the rank scores of the categories. Different from traditional query suggestion which is extracted from query logs,we extract suggestion terms from snippets. We apply our relatedness measures to the query suggestion. Using the proposed relatedness measures, our query suggestion extracted shows a high agreement of relatedness. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-15T02:31:34Z (GMT). No. of bitstreams: 1 ntu-98-D91922022-1.pdf: 1820763 bytes, checksum: f4d7303d52494bb7f5ccdd082f253482 (MD5) Previous issue date: 2009 | en |
| dc.description.tableofcontents | 誌謝 i
摘要 iii Abstract v Table of Contents vii List of Tables x List of Figures xii Chapter 1 Introduction 1 1.1 Motivation 1 1.2 A Web-based Metric by Conditional Query 4 1.3 Community Chain Detection 6 1.4 Query Suggestion 9 1.5 The Goal of the Study 9 Chapter 2 Approaches to the Relatedness Measure 11 2.1 Three Benchmark Datasets 11 2.2 Thesaurus-based Approaches 12 2.3 Corpus-based Approaches 16 2.4 Wikipedia-based Approaches 16 Chapter 3 A Web-based Relatedness Measure by Conditional Query 19 3.1 Introduction 19 3.2 Frequency Information by Conditional Query 21 3.3 Association of Common Words 25 3.4 Association of Named Entities 28 3.5 Applications 34 3.5.1 An NE Ontology Generation Engine 34 3.5.2 Disambiguation Using Association of Named Entities 39 3.6 Discussion 43 Chapter 4 The Enhanced Relatedness Measure and Its Application on Community Chain Detection and Query Suggestion 45 4.1 Introduction 45 4.2 Web-based Relatedness Measure 48 4.2.1 Mutual dependency between objects 48 4.2.2 Transfer Functions with the Dependency Score 50 4.3 Bag of Words Model 52 4.4 The Computation of Word Level Relatedness 54 4.4.1 The Results of the Bag of Words Model 55 4.4.2 Optimal Strategy Parameters 57 4.4.3 Experiment Results 62 4.4.4 Comparisons with the Related Works and Summaries 68 4.5 Community Chain Detection 71 4.5.1 Link Detection Test 71 4.5.2 Community Chain Clustering 72 4.5.3 Experiments of the Community Chain Detection 74 4.5.4 Experiments of Community Chain Clustering 79 4.6 Application: Query Suggestion 80 4.7 Discussion 83 Chapter 5 Labeling Categories and Relationships in an Evolving Social Network 85 5.1 Introduction 85 5.2 Evolving Social Network 86 5.3 Building a Directed Graph by the ODP Resource 89 5.3.1 Cue Patterns 89 5.3.2 Building a Directed Graph 89 5.4 Extracted Critical Nodes from a Directed Graph 91 5.5 Experiments 94 5.5.1 Evolving Social Networks 94 5.5.2 Extracting Potential Categories 95 5.5.3 Labeling Relationships 98 5.6 Discussion 100 Chapter 6 Conclusions and Future Work 101 6.1 Achievements 101 6.2 Future Work 102 References 105 Appendixes 111 Appendix A. 100 Transliteration Names 111 Appendix B. The TC-353 Testing dataset 112 Appendix C. Some Examples of the Query Suggestion 116 | |
| dc.language.iso | en | |
| dc.subject | 相關性量測 | zh_TW |
| dc.subject | 演化中的社群網路 | zh_TW |
| dc.subject | 關係標記 | zh_TW |
| dc.subject | 類別標記 | zh_TW |
| dc.subject | 查詢詞推薦 | zh_TW |
| dc.subject | 社群偵測 | zh_TW |
| dc.subject | Query Suggestion | en |
| dc.subject | Relatedness Measure | en |
| dc.subject | Evolving Social Network | en |
| dc.subject | Relationships Labeling | en |
| dc.subject | Category Labeling | en |
| dc.subject | Community Chain Detection | en |
| dc.title | 以網際網路語料為基礎之相關性量測研究及其在社群偵測與查詢詞推薦之應用 | zh_TW |
| dc.title | A Study on Web-based Relatedness Measure and Its Applications on Community Chain Detection and Query Suggestion | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 97-2 | |
| dc.description.degree | 博士 | |
| dc.contributor.oralexamcommittee | 傅楸善(Chiou-Shann Fuh),張俊盛(Jason-S. Chang),陳克健(Keh-Jiann Chen),梁婷(Tyne Liang),吳宗憲(Chung-Hsien Wu),曾元顯(Yuen-Hsien Tseng) | |
| dc.subject.keyword | 相關性量測,社群偵測,查詢詞推薦,類別標記,關係標記,演化中的社群網路, | zh_TW |
| dc.subject.keyword | Relatedness Measure,Community Chain Detection,Query Suggestion,Category Labeling,Relationships Labeling,Evolving Social Network, | en |
| dc.relation.page | 117 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2009-08-17 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
| 顯示於系所單位： | 資訊工程學系 | |
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