如同一個英文單字可能有數種不同的解釋意思， 一種解釋意思也可能被表示成數個不同的英文單字。因此，對於在文句中詞義模糊的單字而言，如何選出最適當的代表意思是自然語言處理技術上的主要問題之一。然而，目前大部份詞義解析的方法若非只能解析部份詞類(如名詞)的單字，就是其解析詞義的精確度仍有賴突破，所造成的詞義模糊情況常令使用者感到困擾。
這篇研究中，提出一個新的利用詞彙網路(WordNet)、語義彙編資料庫檔案(SemCor)和全球資訊網(the Web)的詞義解析方法，試圖明確解析文句中不同詞類的單字，除了名詞之外，也能解析句子中的動詞、形容詞和副詞的模糊詞義。從語義彙編資料庫中隨機選擇文件檔案與字句，藉由測量詞對中詞義之間的語義相似度，篩選出詞對中目標詞可能的適當候選詞義；根據詞彙網路所提供的同義詞集資料庫，利用加權的機制考量同義詞之間可能的詞義差異，以決定候選詞義的對應同義詞集；並且使用對應同義詞集中的詞義以延伸候選詞義，再合併內文視窗技術形成查詢字串，以該查詢字串傳送到搜尋引尋搜尋全球資訊網上相關的文件之後，藉由找到的相關文件數量排序候選詞義，以判斷目標詞的最佳詞義。
本研究利用布朗大學建立的語義彙編資料檔作為效能評估的資料來源， 實驗結果顯示本方法解析名詞、動詞、形容詞和副詞等四種詞類之詞義，於使用第一個選擇詞義時的平均精確度為81.3% ， 略優於Stetina et al.方法的80%和Mihalcea et al.方法的80.1%，且是其中唯一解析動詞的平均精確度達到70%的方法。若使用前三個選擇詞義時，本方法對該四種詞類的平均精確度超過96%，遠優於其它二種方法。本方法可望改善利用詞義分析技術的應用，例如機器翻譯、文件分類或資訊擷取等的效能。

As a single English word can have several different meanings, a single meaning can be expressed by several different English words. The meaning of a word depends on the sense intended. Thus to select the most appropriate meaning for an ambiguous word within a context is a critical problem for the applications using the technologies of natural language processing. However, at present, most word sense disambiguation methods either disambiguate only restricted parts of speech words such as only nouns or the accuracy in disambiguating word senses is not satisfiable. The ambiguous situation often bothers users.
In this study, a new word sense disambiguation method using WordNet lexicon database, SemCor text files, and the Web is presented. In addition to nouns, the proposed method also attempts to disambiguate verbs, adjectives, and adverbs in sentences. The text files and sentences investigated in the experiments were randomly selected from SemCor. The semantic similarity between the senses of individually semantically ambiguous words in a word pair is measured to select the applicable candidate senses of a target word in that word pair. By a synonym weighting method, the possible sense diversity in synonym sets is considered based on the synonym sets WordNet provides. Thus corresponding synonym sets of the candidate senses are determined. The candidate senses expanded with the senses in the corresponding synonym sets, and enhanced by the context window technique form new queries. After the new queries are submitted to a search engine to search for the matching documents on the Web, the candidate senses are ranked by the number of the matching documents found. The first sense in the list of the ranked candidate senses is viewed as the most appropriate sense of the target word.
The proposed method as well as Stetina et al.’s and Mihalcea et al.’s methods are evaluated based on the SemCor text files. The experimental results show that for the top sense selected this method having the average accuracy of disambiguating word senses with 81.3% for nouns, verbs, adjectives, and adverbs is slightly better than Stetina et al.’s method of 80% and Mihalcea et al.’s method of 80.1%. Furthermore, the proposed method is the only method with the accuracy of disambiguating word senses for verbs achieving 70% for the top one sense selected. Moreover, for the top three senses selected this method is superior to the other two methods by an average accuracy of the four parts of speech exceeding 96%. It is expected that the proposed method can improve the performance of the word sense disambiguation applications in machine translation, document classification, or information retrieval.

CHAPTER 1 INTRODUCTION 1
CHAPTER 2 THE DEVELOPMENT RESOURCES 4
2.1 WORDNET 4
2.2 SEMCOR 8
CHAPTER 3 RELATED WORK 11
CHAPTER 4 THE WORD SENSE DISAMBIGUATION METHOD 16
4.1 SELECTING THE CANDIDATE SENSES 19
4.1.1 Semantic Similarity Measure 20
4.2 REDUCING THE SYNONYM SETS OF THE CANDIDATE SENSES 27
4.2.1 The Synonym Weighting Method 28
4.3 CONTEXTUAL RANKING OF THE CANDIDATE SENSES 35
4.3.1 Candidate Sense Expansion 36
4.3.2 Gathering Documents from the Web 39
CHAPTER 5 PERFORMANCE EVALUATION 40
5.1 EXPERIMENTAL ENVIRONMENT 40
5.2 EXPERIMENTAL RESULTS 45
5.3 DISCUSSION 48
CHAPTER 6 CONCLUSIONS 49
REFERENCES 50

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