傳統的線上知識分享環境存在著大量的知識文件或討論文件。因此，一項組織知識分享的重要議題是當面對大量的知識文件如何做有效率的管理。而我們認為在知識文件間可能存在著外顯或內隱的回應-語意關係（reply-semantic relationships）。然而，這樣的回應-語意關係一旦被發現或辨認出來，藉由這樣一個更先進且具有語意擷取能力的機制，將大大提升企業知識存取的能力。在本研究中，我們針對回應式的文件，提出一組回應-語意關係的初步類別架構，且發展一項知識文件間語意關係辨識技術（SEmantic Enrichment between Knowledge documents, SEEK），以自動化辨識回應式文件間的語意關係。
　　本研究以內容式文件分類技術(content-based text categorization)以及文類分類技術(genre classification)為基礎，我們設計並評估不同特徵組合而成的模式、包括：關鍵字特徵（keyword features）、POS統計值特徵（POS statistics features）、及給定/新發現資訊特徵（given/new information features, GI/NI）的組合。根據實證評估的結果顯示，我們所提出的SEEK技術可以達到一個不錯的分類精準度。此外，以關鍵字與GI/NI的組合做為特徵值用於本技術，於Answer/Comment語意關係分類工作有最佳的分類準確率。另一方面，只使用關鍵字做為特徵值用於本技術，於Explanation/Instruction語意關係分類工作表現最好。

A typical online knowledge-sharing environment would generate vast amount of formal knowledge elements or interactions that generally available as textual documents. Thus, an effective management of the ever-increasing volume of online knowledge documents is essential to organizational knowledge sharing. Reply-semantic relationships between knowledge documents may exist either explicitly or implicitly. Such reply-semantic relationships between knowledge documents, once discovered or identified, would facilitate subsequent knowledge access by providing a novel and more semantic retrieval mechanism. In this study, we propose a preliminary taxonomy of reply-semantic relationships for documents organized in reply-replied structures and develop a SEmantic Enrichment between Knowledge documents (SEEK) technique for automatically annotating reply-semantic relationships between reply-pair documents. Based on the content-based text categorization techniques and genre classification techniques, we propose and evaluate different feature-set models, combinations of keyword features, POS statistics features, and/or given/new information (GI/NI) features. Our empirical evaluation results show that the proposed SEEK technique can achieve a satisfactory classification accuracy. Furthermore, use of keyword and GI/NI features by the proposed SEEK technique resulted in the best classification accuracy for the Answer/Comment classification task. On the other hand, the use of keyword features only can best differentiate Explanation and Instruction relationships.

CHAPTER 1 INTRODUCTION 1
1.1 Background and Research Motivation 1
1.2 Research Objective 3
1.3 Organization of the Thesis 4
CHAPTER 2 LITERATURE REVIEW 6
2.1 Schemes for Communicative Actions 6
2.1.1 Speech Act Schemes 6
2.1.2 Classification Scheme for Relationships between Messages in A Reply-Replied Structure 11
2.2 Text Categorization 12
2.3 Genre Classification 16
CHAPTER 3 DESIGN OF SEMANTIC ENRICHMENT FOR KNOWLEDGE DOCUMENTS (SEEK) TECHNIQUE 21
3.1 A Taxonomy of Reply-semantic Relationships in SEEK 21
3.2 Overall Process of SEmantic Enrichment for Knowledge documents (SEEK) Technique 25
3.2.1 Cue Feature Extraction and Selection Phases 26
3.2.2 Reply-pair Representation Phase 30
3.2.3 Induction Phase 30
CHAPTER 4 EMPIRICAL EVALUATION FOR SEEK TECHNIQUE 31
4.1 Evaluation Design 31
4.1.1 Data Collection 31
4.1.2 Evaluation Criteria 33
4.1.3 Evaluation Procedure 33
4.2 Evaluation Results 33
4.2.1 Tuning Number of Keywords for Model 1 to Model 4 34
4.2.2 Comparative Evaluation of SEEK 36
CHAPTER 5 CONCLUSION AND FUTURE RESEARCH DIRECTIONS 39
APPENDIX A: PENN TREEBANK TAGS 42
REFERENCES 45

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