中文動詞上下位關係自動標記法

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(1)Automatic Labeling of Hypernymy-Troponymy Relation for Chinese Verbs 中文動詞上下位關係自動標記法. 國立台灣師範大學英語學系碩士論文 Master Thesis Department of English, National Taiwan Normal University 指導教授: 謝舒凱博士 Advisor: Dr. Shu-Kai Hsieh. 研究生: 羅巧珊 Student: Chiao-Shan Lo 中華民國九十八年七月 July, 2009.

(2) Abstract 近年來，詞彙網路(Wordnet)已成為計算語言學相關領域中最為普遍利用的資源之一，對於資訊檢索(Information Retrieval)或是自然語言處理(Natural Language Processing)的發展有相當大的幫助。詞彙網路是由同義詞集(Synset)以及詞彙語意關係(Lexical Semantic Relation)所建構而成，例如以英語為主的普林斯頓詞網(Princeton WordNet)、以及結合多個歐洲語言的歐語詞網(EuroWordNet)等，建構皆已相當完善。然而，一個詞網的建構並非一時一人之力所能完成，其所需要的人力以及耗費的時間相當可觀。因此，如何有效率並有系統的建構一個詞網是近年來研究致力的目標。而詞彙間的語意關係是構成一個詞網的主要元素，因此，如何自動化的抽取詞彙語義關係是建構詞網的重要步驟之一。中研院語言所已建立一個以中頻詞為主的中文詞彙網路(Chinese WordNet, CWN)，旨在提供完整的中文辭彙之詞義區分。然而，在目前中文詞彙網路系統中，同義詞集間相互的語意關係乃是採用人為判定標記，且這些標記之數量尚未達成可行應用之一定規模。因此，本研究提出一套半自動化的方法來自動標記詞彙間的語意關係，本篇論文針對動詞之間的上下位詞彙語意關係(Hypernymy-troponymy relation)，提出一種自動標記的方法，並抽取具有中文上下位關係之中文動詞組對。. 本篇論文提出兩種並行之方法，第一，藉由句法上特定的句型(lexical syntactic pattern)，自動抽取出中文詞彙網路中具有上下位關係之動詞組。第二，我們利用bootstrapping的方法，透過中研院建構的中英雙語詞網(Sinica Bow)大量將普林斯頓英語詞網中的語意關係對映至中文。實驗結果顯示，此系統能快速並大量地自動抽取出具有上下位語意關係之中文動詞組，本論文盼能將此方法應用於正在發展中的中文詞網自動語意關係標記，以及知識本體之自動建構，進而能有效率的建構完善的中文詞彙知識資源。關鍵詞：語義關係自動標記、動詞詞彙語意、動詞上下位關係、中文詞網.

(3) Abstract. WordNet-like databases have become crucial sources for lexical semantic studies and computational linguistic applications such as Information Retrieval (IR) and Natural Language Processing (NLP). The fundamental elements of WordNet are synsets (the synonymous grouping of words) and semantic relations among synsets. However, creating such a lexical network is a time-consuming and labor-intensive project. In particular, for those languages with few resources such as Chinese, is even difficult. Chinese WordNet (CWN), which composed of middle frequency words, has been launched by Academia Sinica based on the similar paradigm as Princeton WordNet. The synset that each word sense locates in CWN is manually labeled. However, the lexical semantic relations among synsets in CWN are only partially constructed and lack of systematic labeling. Therefore, in this thesis, two independent approaches were proposed to automatically harvesting lexical semantic relations, especially focused on the hypernymy-troponymy relation of verbs.. This thesis describes two approaches for discovering hypernymy-troponymy relation among verbs. Syntactic pattern-based approach is used for that sentence structures can always denote relations and reveal information among lexical entries. Bootstrapping approach, on the other hand, aims at exploiting an already existing database and combining them within a common, standard framework. From a large scale of input data, our proposed approaches can greatly and rapidly extract verb pairs that are in hypernymy-troponymy relation in Chinese, aiding the construction of lexical database in a more effective way. In addition, it is hoped that these approaches will shed light on the task of automatic acquisition of other Chinese lexical semantic relations and ontology learning as well. Key word: automatic extraction, lexical semantic relation, troponymy, Chinese WordNet i.

(4) ACKNOWLEDGEMENTS 終於到了寫謝詞的這一刻，從開始撰寫論文沒多久我就一邊構思著謝詞的內容，因為一路走來，實在有太多人要感謝了。寫論文是一段漫長又煎熬的過程，這段過程中常常遇到各種挑戰與瓶頸，總是讓我灰心不已。不過很幸運地，這一路上總是有許多人伸出援手，不論是學術上的見解，或是精神上的支持，都給予我莫大的幫助。在此，我要向這些人深深表達我由衷的感謝。首先，我要感謝我的指導教授，謝舒凱老師。碩二時，我擔任老師的助理，並且修了老師在研究所開的每一堂課，對計算語言學的認識可說是受到老師的啟蒙，讓我接觸到了語言學另一個全新的領域。在寫論文的過程中，老師總是給我非常自由的空間去發揮，並且對我提出的疑問跟想法都給予解答與支持，而謝老師沉穩的個性也是最能安定人心的力量，每當我因為遇到瓶頸而焦躁不安時，老師總是有辦法不疾不徐地協助我解決困難。另外，我也要感謝我的兩位口試委員：台大外文系的高照明老師，以及政大英語系的鍾曉芳老師。高老師在我兩次口試時，總是提出許多精闢的見解，不論是在語言學方面或是計算程式方面，都給了我很多很實用的建議，口試結束後高老師更是熱心地提供我需要的資源並回答我的疑問。而鍾老師也是在百忙之中抽空前來擔任口試委員，儘管如此，鍾老師還是在我的論文裡，密密麻麻地寫下她的意見並點出論文的缺點。感謝兩位老師的幫忙，這篇論文才能完成。我也要感謝師大每位優秀的老師及同學，謝謝每位曾經教過我的老師，在師大的每一門課都是既紮實又豐富，一點一滴累積我對語言學的知識以及撰寫論文的能力。還有班上優秀的同學們，Nancy, Caroline, David, Fu-Pin, Clara, and Jessica 等等，雖然到了後來大家因為工作或論文，各自努力很少見面，但是我們仍然會在空閒時交換心得，給彼此鼓勵。除此之外，我要由衷的感謝中研院語言所的程式設計師，李龍豪先生。如果沒有你熱心的 ii.

(5) 幫忙，這篇論文不可能完成。還要感謝中研院的怡頻、俞庭以及淳涵，感謝你們在百忙之中放下手邊的工作來協助我分析那上千筆的語料。我也要感謝我的戰友們: 儷蓉、書瑋、徐瑜。寫論文有多痛苦，真的要寫過才知道，好險那些難熬的日子裡有你們的陪伴，互吐苦水，相互勉勵。最後，也是最重要的，我要感謝我的家人，感謝爸媽總是無條件的在背後支持我，不論是在精神上或是物質上都給我莫大的幫助，還有妹妹不時捎來的關心與問候，都讓我倍感窩心。謝謝你們一路陪我走過來，支持我所做的每一個決定，謹以這本論文獻給你們---- 我最愛的家人。. iii.

(6) Contents. 1. 2. Introduction. 1. 1.1. Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 1. 1.2. Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 3. 1.3. Organization of the Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 4. Related Works. 5. 2.1. WordNet-like Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 5. 2.1.1. Princeton WordNet [31] . . . . . . . . . . . . . . . . . . . . . . . . .. 6. 2.1.2. EuroWordNet [45] . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 7. 2.1.3. Sinica Bow [23] . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 8. 2.1.4. Chinese WordNet [1] . . . . . . . . . . . . . . . . . . . . . . . . . . . 10. 2.1.5. HowNet [14] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12. 2.2. 2.3. Semantic Relations of Verbs . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.2.1. Semantic Relations of Verbs in WordNet . . . . . . . . . . . . . . . . 13. 2.2.2. Semantic Relations of Verbs in EuroWordNet . . . . . . . . . . . . . . 16. 2.2.3. Other Relations of Verbs . . . . . . . . . . . . . . . . . . . . . . . . . 20. Troponymy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 2.3.1. Definition of Troponymy . . . . . . . . . . . . . . . . . . . . . . . . . 24. iv.

(7) 2.3.2 2.4. 2.5. 3. Automatic Discovery of Lexical Semantic Relation . . . . . . . . . . . . . . . 28 2.4.1. Lexico Syntactic Pattern–Based Approach . . . . . . . . . . . . . . . . 29. 2.4.2. Clustering-Based Approach . . . . . . . . . . . . . . . . . . . . . . . 32. 2.4.3. Bootstrapping Approach . . . . . . . . . . . . . . . . . . . . . . . . . 33. Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35. Methodology 3.1. 3.2. 3.3. 3.4. 4. Distinguishing Manner . . . . . . . . . . . . . . . . . . . . . . . . . . 26. 37. Syntactic Pattern-Based Approach . . . . . . . . . . . . . . . . . . . . . . . . 37 3.1.1. Database: Chinese WordNet . . . . . . . . . . . . . . . . . . . . . . . 37. 3.1.2. Data Pre-processing . . . . . . . . . . . . . . . . . . . . . . . . . . . 39. 3.1.3. Syntactic Patterns in Chinese . . . . . . . . . . . . . . . . . . . . . . . 41. 3.1.4. Procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42. Bootstrapping Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 3.2.1. Data Source . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46. 3.2.2. Procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48. Evaluation and Scoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 3.3.1. Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50. 3.3.2. Scoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54. Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55. Results and Error Analyses 4.1. 56. Results from Syntactic Pattern- based Approach . . . . . . . . . . . . . . . . . 56 4.1.1. Error Analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58. 4.1.2. Interim Summary. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68. v.

(8) 4.2. Results from Bootstrapping Approach . . . . . . . . . . . . . . . . . . . . . . 69 4.2.1. 4.3. 4.4. 5. Error Analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70. Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 4.3.1. Comparison of Two Approaches . . . . . . . . . . . . . . . . . . . . . 81. 4.3.2. Comparison of the Results . . . . . . . . . . . . . . . . . . . . . . . . 83. 4.3.3. Comparison of the Error Types . . . . . . . . . . . . . . . . . . . . . . 86. 4.3.4. General Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89. Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91. Conclusion. 92. 5.1. Summary of the Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92. 5.2. Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94. 5.3. Limitations of the Present Study and Suggestions for Future Work . . . . . . . 95. Appendix:. A Programming Code. 104. B Results from Syntactic Pattern-based Approach. 107. C Results from Bootstrapping Approach. 110. vi.

(9) List of Tables 2.1. A finer-grained semantic relation among verbs. [9] . . . . . . . . . . . . . . . 21. 2.2. Semantic relations of verbs in Wordnet, EuroWordNet and VerbOcean . . . . . 23. 2.3. Three different types of Troponymy . . . . . . . . . . . . . . . . . . . . . . . 28. 4.1. General results of syntactic pattern-based approach . . . . . . . . . . . . . . . 57. 4.2. Error types and percentage . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59. 4.3. Overall results from bootstrapping approach . . . . . . . . . . . . . . . . . . . 70. 4.4. Non hypernymy-troponymy verb pairs (Total number of returned verb pairs= 11289) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71. 4.5. General comparison of syntactic pattern-based and bootstrapping approach . . 82. 4.6. Comparison of error types from results in two approaches . . . . . . . . . . . . 86. 4.7. General comparison of the two approaches. vii. . . . . . . . . . . . . . . . . . . . 89.

(10) List of Figures 2.1. The first two senses returned by CWN of the verb 走 ‘zao3, walk’ . . . . . . . 11. 2.2. Four kinds of entailments among English verbs [31] . . . . . . . . . . . . . . . 15. 2.3. Translation-mediated LSR Prediction (The complete model) . . . . . . . . . . 33. 2.4. Translation-mediated LSR Prediction (when translation equivalents are synonymous) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34. 3.1. Bootstrapping model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45. 3.2. Overall procedure of bootstrapping approach . . . . . . . . . . . . . . . . . . 50. viii.

(11) Chapter 1 Introduction. 1.1. Background. In recent years, there has been an increasing focus on the construction of lexical knowledge resources in the field of Natural Language Processing (NLP), such as Thesaurus, WordNets [31], EuroWordNet [45], FrameNet [6], HowNet [13], etc. Among these resources, Princeton WordNet1 , an electronic English lexical database, was started as an implementation of a psycholinguistic model of the mental lexicon. In WordNet, English nouns, verbs, adjectives, and adverbs are organized into synonym sets, called synsets. Synsets in WordNet are connected with each other by various kinds of paradigmatic lexical semantic relations, such as Meronymy and Holonymy (between parts and wholes), Hypernymy and Hyponymy (between specific and more general synsets), etc. These relations act as pointers between synsets. Due to the semantic relation-based property, WordNet has been widely used to solve a variety of problems in the field of NLP and has sparked off most interest both in theoretical and applicational sides, such as Information Retrieval (IR), lexical acquisition, automatic extraction, Word Sense Disambiguation (WSD), and so on. WordNet’s growing popularity has prompted the modeling and 1. http://wordnet.princeton.edu. 1.

(12) construction of wordnets in other languages and various domains as well. EuroWordNet [45], which aims to build a multilingual database for several European languages, is a successful example. To date, in the field of NLP applications, WordNet and EuroWordNet serve as very crucial sources and have become a standard norm in evaluating semantic relations. WordNet covers a large scale of sense-based English lexicons (206941 word-sense pairs 2 ). The extensive coverage of WordNet took immense labors and time. Further, semantic relations are unlimited, it takes years and intensive labors to steadily develop the scope and content. Consequently, there has been significant recent interest in finding methods to build a WordNet-like database in other languages with less efforts and time [5] [7] [9] [20] [21] [24] [28] [30] [32] [38] [39].. Lexical semantic relations among synsets are the foundations of a semantic network, but manually constructing all the relations is time-consuming and error-prone. Therefore, one of the most important steps toward efficiently constructing a WordNet-like database is to automatically extract lexical semantic relations. It is clearly necessary to develop an automated approach to harvest semantic relations; hence motivates researchers working on automated methods paralleled with manual verification, in order to ease the work. To date, most research on semantic relation harvesting have focused on is-a (c.f. [21] [32]) and part-of (c.f. [7] [20]) relations of nouns. Verb relations, on the contrary, have been studied less often especially the is-a relation among verbs– the hypernymy-troponymy relation. Several approaches have been suggested for automatically extracting semantic relations and they mainly fall onto three categories: lexico syntactic pattern-based approach, clustering-based approach, and bootstrapping approach, all of which will be introduced in this thesis. 2. The statistics is updated to WordNet3.0. 2.

(13) 1.2. Motivation. As mentioned, creating a lexical semantic knowledge resource like WordNet is a time-consuming and labor-intensive task. Languages other than English and some European languages are facing with the lack of long-term linguistic supports, let alone those languages without balanced corpus available. In Chinese, constructing a wordnet is comparatively difficult owing to the fuzzy definition and classification among words, morphemes, and characters. The Chinese WordNet (hereafter CWN)3 , created by Academia Sinica, aims to provide complete sense inventory for each word based on the theory of lexical semantics and ontology. The synsets of each word in CWN are manually labeled but the semantic relations among synsets are only left partially constructed. In English or other European languages, several approaches were proposed to find semantic relations automatically such as lexico syntactic pattern-based approach [21], clustering approach [27] and bootstrapping approach [39]. However, in Chinese, there is few research studying on this issue. Huang et al. [24] has proposed a bootstrapping method mapping English WordNet to Chinese WordNet but the experimental data is small (about 200 lemmas) and several translational idiosyncrasies were ignored. Hence, bootstrapping method alone is not accredited enough. What is more, to the best of our knowledge, there is not yet a study focusing on the extraction of hypernymy-troponymy relation in any languages. The above mentioned issues reveal the need and the lack of a semantic relation-based wordnet in Chinese. This motivates this thesis to propose a lexico syntactic pattern-based approach assisted with bootstrapping to automatically label the semantic relations. Although the target in this thesis focuses only on hypernymy-troponymy relation of verbs, it is hoped that the results can serve as a useful framework for the subsequent studies on the extraction of semantic relations. 3. http://cwn.ling.sinica.edu.tw/. 3.

(14) 1.3. Organization of the Thesis. The reminder of this thesis is organized as follows. Chapter 2 describes the relevant issues about lexical semantic databases and approaches on automatically extracting lexical information. It starts with an introduction of WordNet-like data resources along with how verb relations are classified in each database. Next, a detailed review on the definition of troponymy will be given. Finally, previous approaches on automated extraction will be reviewed and compared. In Chapter 3, we will introduce the two approaches adopted in this thesis including data sources, algorithm design, and procedure. Evaluation standard and scoring will also be presented in this chapter. Chapter 4 reports the results returned by each approach along with statistical representation and in-depth error analyses. The comparison of these two approaches will be discussed as well. Finally, Chapter 5 summarized the thesis along with our contribution and suggestions for future work.. 4.

(15) Chapter 2 Related Works This chapter devoted to the previous relevant studies and the introduction of some existing databases. In Section 2.1, I will begin with the introduction of several influential lexical semantic networks including Princeton WordNet, EuroWordNet, Chinese WordNet and Sinica Bilingual Ontological Wordnet. Section 2.2 turns the attention to the semantic relations of verbs which have different ways of classification in different Wordnet databases. Section 2.3 focuses on the target relation of this thesis–troponymy, which is the hypernymy-hyponymy relation among verbs. Finally, in Section 2.4, different approaches of discovering semantic relations in previous works will be discussed.. 2.1. WordNet-like Resources. Lexical semantic resource has been considered vital resources in Natural Language Processing (NLP). In recent years, there has been an increasing focus on the construction of lexical knowledge resources such as Princeton WordNets [31], EuroWordNet [45], Mindnet [42], HowNet [13], VerbNet [26] etc. In this section, lexical semantic networks which are closely related to the this thesis will be introduced. Princeton WordNet, EuroWordNet, Sinica Bilin5.

(16) gual Ontological Wordnet, Chinese WordNet and HowNet will be introduced respectively in the following sections.. 2.1.1. Princeton WordNet [31]. Launched in the early 1980s at Princeton University, WordNet has become a popular and crucial semantic network of English lexicon. Comprising more than 200,000 word meaning pairs, WordNet has grown to become an extensive electronic dictionary of English language [16]. Basically, WordNet differs from traditional alphabetical dictionaries in that it describes words with other words in a highly systematic way, mapping languages to concepts; also in that it organizes lexical information in terms of word meanings, but not word forms. WordNet organizes words into groups of near synonyms called synsets and synsets together define a unique sense (concept). What is more, in WordNet, the synsets are interlinked by means of bidirectional semantic relations such as meronymy, hypernymy and entailment, etc. Synsets and semantic relations, therefore, together serve as foundation of WordNet and set up this database. The semantic relations not only hold between words, but also between words and synsets, and between synsets themselves. The truth that WordNet is formed by synsets implies that only open class words in English are covered in WordNet since words in different categories (function words) cannot form synsets, as they are never interchangeable within a linguistic context. Therefore, WordNet contains only content words: Nouns, Verbs, Adjectives, and Adverbs. The following is quoted from Miller et al. in [31] defining the structures of each category. “Nouns are organized in lexical memory as topical hierarchies, verbs are organized by a variety of entailment relations, and adjectives and adverbs are organized as Ndimensional hyperspaces.” 6.

(17) Started as an implementation of a psycholinguistic model of the mental lexicon, WordNet produces a combination of dictionary and thesaurus that is more intuitively usable. By the same token, WordNet has sparked off most interest both in theoretical and applicational sides, especially in NLP and IR. There are various off springs mapping WordNet’s achievements onto languages other than English. Wordnets of European languages and Chinese, for example, are launched under similar spirit and will be discussed in the following sections.. 2.1.2. EuroWordNet [45]. Being structured along the same lines as Princeton WordNet (specifically WordNet version 1.5), EuroWordNet is characterized by its multi-lingual nature. EuroWordNet is an integrated system that contains several European languages in which every sub-wordnet is a monolingual database. Each monolingual sub-wordnet represents a language –internal system constructed by synsets and basic semantic relations such as antonym, hyponym, meronym, etc. Besides, the equivalence relations between synsets in different languages and Princeton WordNet will be connected via the Inter-Lingual-Index (ILI) [46]. Therefore, by expanding words in one language to related words in another language via ILI, EuroWordNet may allow easy accessing of retrieving information stored in various languages. Also, the availability of multilingual makes each sub-wordnet compatible and comparable. Hence, not only the language- independent data is shared but also the language-specific differences can be maintained as well. Besides EuroWordNet’s multilinguality which makes it distinct from Princeton WordNet, there are still some changes and additions in the EuroWordNet. One of the most distinct features is the explicit semantic relations across parts-of-speech (POS). In Princeton WordNet, nouns and verbs are not interrelated with each other by semantic relations; consequently, sometimes very similar synsets tend to be totally unrelated only because they belong to different POS. The following is. 7.

(18) an example given in [44]: the noun adornment and the verb adorn expresses the same concept but they are not connected in WordNet for the different POS. On the other hand, words with different POS in EuroWordNet can be inter-linked with explicit synonymy and hyponymy relations. Therefore, the semantic relation between the noun adornment and the verb adorn can be described explicitly in EuroWordNet as follows where XPOS represents cross parts-of-speech: {adore, V}. XPOS_NEAR_SYNONYM. {adornment, N}. Semantic relations used in Princeton WordNet were also adjusted in EuroWordNet. The most important relations of the Princeton WordNet have been maintained but extended in some ways [45]. First, for example, relations can have features; therefore, labels such as conjunction, factive, reversed and negation can be added to relations. Second, some existing relations have been broadened, like a more global near-synonym relation was proposed in EuroWordNet. Third, new semantic relations have been added as well; role-relations were used between entities and events. More detailed description of semantic relations in EuroWordNet will be introduced in the following section when discussing semantic relations among verbs.. Translingual knowledge is very important in the network era since uncountable information is disseminated across languages and boundaries via Internet; therefore, linguistic resources such as a multilingual thesaurus are indispensable for Information Retrieval. EuroWordNet is a successful example in following the paradigm of Princeton WordNet and also extend the design idea to the multilingual nature.. 2.1.3. Sinica Bow [23]. Sinica Bow represents the Academia Sinica Bilingual Ontological Wordnet. It is a bilingual thesaurus integrated three resources: WordNet, English-Chinese Translation Equivalents Database 8.

(19) (ECTED), and Suggested Upper Merged Ontology (SUMO) [23]. Sinica Bow adopted WordNet version 1.6 and 1.7.1 which are used by most application so far [2]. And through ECTED, which is a crucial equivalence translation tool, a Chinese WordNet is bootstrapping from English WordNet. Constructed at Academia Sinica, the translation equivalence database was hand-crafted by a group of people. All Possible translations of an English synset word were extracted from several online bilingual (CE or EC) dictionaries. Afterwards, all the translated candidates will be checked by linguists who have near native-like ability in both languages. Finally, the translator selected three most appropriate translations whenever possible and tried to possess each translated entries into lexicalized words rather than descriptive phrases [25]. Another resource of Sinica Bow is SUMO, which represents an upper ontology constructed by Pease et al. [35] [34]. SUMO covers very general concepts such as time, spatial relations, physical objects, events and processes; the aim of SUMO is to link categories and relations coming from different top-level ontologies and to formalize different domains into a set of concepts, relations and axioms. SUMO can be applied onto the natural language processing, information retrieval, automated reasoning, and also the inter-operability in E-commerce, helping computers manage more details and structures. The three above resources together comprise the Sinica Bilingual Ontological Wordnet, which allow a versatile access of lexicon information combining lexical, semantic, and ontological information. Therefore, searching on Sinica Bow can return the following information: sense-based English-Chinese translation, semantic relations, English word-sense based ontology and inference, Chinese word-based ontology and inference.. Under the assumption that lexical semantic relations are universal [45] [16], they can be transported cross-lingually through accurate translation and mapping [24]. Therefore, with the integration of three key resources: ECTED, WordNet, and SUMO, Sinica Bow can plausibly. 9.

(20) function as a medium for mapping WordNet to a Chinese one.. 2.1.4. Chinese WordNet [1]. Creating a semantic relation database is a time-consuming and labor-demanding task. Creating a semantic relation-based wordnet in Chinese is even more difficult owing to the fuzzy definition and classification among words, morphemes, and characters. The Chinese WordNet (CWN) has been launched by Academia Sinica in 2006 and is continuously broadened its scope so far. Unlike some bilingual semantic databases such as Sinica Bow or HowNet, Chinese WordNet starts from Chinese per se but not through translating from another language. Lemmas included in Chinese WordNet mainly fall on the medium frequency words which are totally composed of 5600 lemmas and 13160 word senses [1]. Unlike Princeton WordNet or EuroWordNet, CWN is not a robust database that constructed mainly on the synsets and semantic relations. Rather, it aims to provide complete meanings and exquisite senses for each lemma based on the theory of lexical semantics and ontology. Also, each lemma has a corresponding English translation and is mapped onto WordNet via Sinica BOW. Figure 2.1, for example, shows the result of the verb 走 ‘zou3, walk’ in Chinese Wordnet. As Figure 2.1 shows, it is apparent that synsets and lexical semantic relations are not systematically labeled in CWN. In fact, the synsets of each word and the semantic relations among lemmas in CWN were manually tagged, and unlike WordNet or EuroWordNet, they have not been completely constructed yet. However, employing only human labor like the original WordNet on the construction of CWN would be wasteful and cannot guarantee compatibility. Hence, how to systematically and automatically discover the semantic relations is a crucial step toward constructing a synset and semantic relation-based Chinese Wordnet. Since CWN is composed of explicit senses of medium frequency words and can be seen as a detailed dictionary, this. 10.

(21) Figure 2.1: The first two senses returned by CWN of the verb 走 ‘zao3, walk’. present study tries to discover a pattern-based method of finding semantic relations (specifically the troponym relation) within CWN. Along with other approaches, such as bootstrapping from other existing WordNet [3] or bilingual dictionaries [25] (these will be introduced later), it is hoped that the results will be more direct and precise. While CWN is, until now, still devoted to extending its lemma from medium frequency words to high frequency words, our automated method on semantic relations extraction is hoped to enrich the completeness and speed up the construction of CWN.. 11.

(22) 2.1.5. HowNet [14]. Finally, another bilingual knowledge database is worth noting. HowNet is an on-line commonsense knowledge base which was constructed since 1988 by Dong [14] [13]. It is a large-scale bilingual lexical ontology for words and their meanings in both Chinese and English. HowNet, in many respect, is similar to Princeton WordNet but the biggest difference that characterizes HowNet and differentiates it from other WordNet-like databases is that HowNet relies less on hierarchical differentiation. In fact, HowNet unveils inter-concept relations and inter-attribute relations of each concepts [13]. It is proposed that the importance of constructing a knowledge system lies in the varied relations amongst concepts as well as those among the attributes of concepts but not taxonomic semantics. Therefore, it is obviously that HowNet and WordNet reflect a different view of semantic organization. As introduced, WordNet differentiates words by putting them under synsets and further differentiates synsets by assigning them to different taxonomy of semantics. On the other hand, HowNet does not provide glosses for each lexical concepts, rather, it combines sememes, which can be seen as the smallest basic semantic unit, from a less discriminating taxonomy to compose a semantic representation of meaning for each word sense. Under this fundamental, the knowledge structured by HowNet is a graph rather than a tree. How HowNet characterizes words can be better grasp by the following example [14]:. doctor|醫生 {human|人: HostOf={Occupation|職位}},domain= {medical|醫}, {docotr|醫治:agent ={˜}}} As can be seen , the lexical concept describes in HowNet is not written in natural language but marked by basic sememes such as {Occupation— 職位} and {medical— 醫}. Despite the fact that HowNet is a powerful knowledge database, this thesis does not resort to it as 12.

(23) one of our data sources. The reason is that HowNet sketches lexical semantic relation in a syntagmatic perspective but not a taxonomic one. Therefore, the target relation in this thesis– the hypernymy-troponymy relation may exclude from this knowledge database.. 2.2. Semantic Relations of Verbs. Although the target of this thesis is only on the hypernymy-troponymy relation among verbs which Fellbaum and Miller in [31] first dubbed as troponymy, yet a thorough review of semantic relations among verbs is needed. There is no consistency in classifying semantic relations of verbs. Therefore, in this section, I will introduce the semantic relations among verbs that defined in WordNet, EuroWordNet and other studies.. 2.2.1. Semantic Relations of Verbs in WordNet. Verbs can not be classified into straightforward taxonomies like nouns do, rather, verbs are divided along semantic fields. Fellbaum in [15] proposed a detailed analysis of relations among verbs and indicates that lexical entailment lies under all verbal relation. WordNet identifies Synonymy, Antonymy, Entailment, Troponymy, and Cause among verb relations. This subsection is going to introduce and review semantic relations of verbs in WordNet. For the troponymy relation, which is the target relation in this thesis, a more detailed and complete review is needed. Therefore, this thesis left troponymy as an independent section and is given in Section 2.3.. Synonym Basically, synset in WordNet indicates synonymous relation since synset is a group of words that together define a unique meaning. However, according to Miller and Fellbaum in [31], 13.

(24) there are few truly synonymous verbs can be found in the lexicon. Most synonymous verbs still have slight differences depending on the context and verbs also differed according to different speech register like in formal, technical, or colloquial environments. Examples are like buy vs. purchase, sweat vs. perspire. Owing to the truth that many apparently synonymous verbs still exhibit trivial differences, verb synsets in WordNet often expressed by periphrastic definitions rather than lexicalized synonyms and it reflects the fact that most verbs are manner elaboration of a more basic verb.. Antonym Generally speaking, antonym is the concept which refers to a pair of words with opposite meaning, however, antonymous pairs of words are not simply ones with opposite meanings, but are suggested to be words with high relatedness both paradigmatically and syntagmatically. In WordNet, the antonym relation is also identified among verbs. Antonymous verb pairs cover the same semantic field and the same activity but differ only in the thematic role such as SOURCE or GOAL (for example give vs. take, sell vs. buy). Similar to synonymous verb pairs, there is few true antonym pairs which complementarily contrast with each other except static verbs and change verbs, such as live vs. die and wake vs. sleep.. Entailment According to Miller [31], the entailment relation underlies all verbal relations:“the different relations that organize the verbs can be cast in terms of one overarching principle– lexical entailment.” Entailment holds between two verbs V1 and V2 when the statement ‘someone V1 s’ entails the statement ‘someone V2 s’. Lexical entailment is a unilateral relation, taking sleep and snore for examples: the proposition that He is snoring entails the proposition He is sleeping, as one cannot snore without sleeping. Therefore, owing to the property of unilateral relation, 14.

(25) the second proposition is necessarily to hold if the first one does but not the other way around. Lexical entailment also includes backward presupposition, such as succeed and try: If a person succeeds to do something, this entails that he has tried to do it.. The differences between verb pairs snore/sleep and succeed/ try lies in the temporal inclusion. Succeed entails try but neither verbs include the other in time, the proposition carries by these two verbs are in order : one succeeds after he tries. However, snore entails sleep and is properly included by it : the time span of snore is properly and entirely included in sleep. Figure 2.2 is a graphic representation of the relations among different kinds of entailment.. Figure 2.2: Four kinds of entailments among English verbs [31]. Cause As Figure 2.2 shows, there are four kinds of entailments distinguished by two features {± temporal inclusion, ± co-extensiveness}. WordNet identifies only Cause relation and Troponymy relation. The latter is the target of this thesis hence will be discussed in detail in 15.

(26) Section 2.3. Cause is a special form of entailment which only applies to temporally disjoint events. Two concepts are mentioned in WordNet, one is a causative verb concept like give, and the other is a resultative concept like have. Causative verbs carry the sense of cause to be/become/happen/have or cause to do. That is to say, they relate transitive verbs to either states or actions. For example, the synset break, bust in WordNet has a causative relation between break, wear, wear out, fall apart which indicate cause to become pieces.. 2.2.2. Semantic Relations of Verbs in EuroWordNet. As mentioned, EuroWordNet is built along the same lines as Princeton WordNet in that synonymous meanings are joined in a synset and language-internal relations are expressed between synsets. However, semantic relations are being adjusted in EuroWordNet. The most distinct feature in EuroWordNet is the explicit cross-part-of-speech (POS) relations. The Princeton WordNet uses a rigid distinction between nouns and verbs, mainly because of their different syntactic role in English. However, this often leads to an undesirable consequence that very similar synsets are totally unrelated only because they belong to different syntactic categories. Therefore, in EuroWordNet, synsets can be inter-linked with one another across POS. That is, verbs do not related with verbs only, but also nouns or adjectives. The following is some important semantic relations of verbs in EuroWordNet [10] [4]:. NEAR SYNONYMY and XPOS NEAR SYNONYMY Unlike WordNet, EuroWordNet used the relation NEAR SYNONYMY for semantically similar words. In many cases, there is a close relation between words but sometimes this relation is not sufficient to group the words into one synset. This is because the hyponyms linked to each of these words can not be exchanged. Therefore NEAR SYNONYMY relation is used in. 16.

(27) EuroWordNet to keep the hyponyms separate and still be able to express that the two words are close in meaning [4]. NEAR SYNONYMY relation is used cross part-of-speech in EuroWordNet, therefore, morphologically very similar words can connect through semantic relations even when they belong to different POS. The followings are examples of verbs in synonymous relation:. (1). {move, V} XPOS NEAR SYNONYMY {movement, N}. (2). {sleep, V} XPOS NEAR SYNONYMY {sleep, N}. HYPERNYMY/HYPONYMY and XPOS HYPERNYMY/HYPONYMY A hyponymy relation implies that the hypernym may substitute the hyponym in a referential context but not the other way around. In EuroWordNet, HAS (XPOS) HYPERNYMY/HYPONYMY relation is used to indicate the relation between a more general class and a more specific subtype either within or across POS. Examples for verbs are as follows:. (3). {move, V} HAS HYPONYMY {walk, V}. (4). {love, V} HAS XPOS HYPERNYMY {emotion, N}. (5). {emotion, N} HAS XPOS HYPONYMY {love, V}. Note that the term ‘troponymy’ is not used in EuroWordNet. Verb pairs in hypernymy/ troponymy relation like walk and move are expressed by HAS HYPERNYMY/HYPONYMY relation in EuroWordNet and it only occurs within one POS.. 17.

(28) ANTONYM and XPOS ANTONYM In EuroWordNet, the Antonym relation referred to a more loosely defined notion which can be defined as the opposition of meaning in a context. As mentioned, antonym between different POS is allowed, as in the cases of synonymy and hypernymy/hyponymy. Antonyms typically form contrasting categories within the same dimension. This means that an antonym not only contrasts with another antonym in certain features but they have to share the same hypernym. This criterion prevents irrelevant pairs such as love and car. The following are examples of antonym relation in EuroWordNet:. (6). {open, V} ANTONYM {close, V}. (7). {live, V} XPOS ANTONYM {dead, A}. CAUSE (XPOS) The cause relation is used in WordNet to indicate the entailed relation between two verbs, one of which referring to an event causing a resulting event, process or state. Whereas in WordNet the causal relation only holds between temporally disjoint verbs, EuroWordNet, on the contrary, applies this relation across syntactic categories. Causal relation in EuroWordNet can be further distinguished with respect to the factivity of the effect. The factive feature denotes that a situation S1 implies the causation of S2 , for example:. (8). {kill, V} XPOS CAUSES {death, N} (factive). (9). {death, N} IS CAUSED BY {kill, V}. 18.

(29) On the other hand, non-factive indicates a situation S1 probably or likely causes S2 or S1 is intended to cause some situation S2 , for example:. (10). {try, V} CAUSES {succeed, V} (Non-factive). From the above examples, EuroWordNet does not further distinguish causal relation and backward presupposition like WordNet does, rather, only causal relation plus the feature of factivity are used in EuroWordNet.. SUBEVENT (XPOS) Recall that Princeton WordNet identifies the relation Entailment on cases that cannot be expressed by the more specific hyponymy or cause relations. In this case the direction of the implication or entailment is indicated. For example, in the case of snore/sleep, the entailment direction is from snore to sleep: snore entails sleep but not the other way around. However, in EuroWordNet, the entailed relation with ‘proper inclusion’ can more adequately be described by means of the SUBEVENT relation which is very useful for many closely related verbs and appeals more directly to human-intuitions.. (11). {snore, V} IS SUBEVENT OF {sleep, V}. (12). {sleep, V} HAS SUBEVENT {snore, V}. ROLE/INVOLVED EuroWordNet identifies additional semantic relation ROLE and INVOLVED which indicates a link between a verb and a noun whose meaning is ‘incorporated’ in, or connected with the. 19.

(30) meaning of the verb itself. Sometimes the most salient relation is not the hypernymy but the relation between the event and the involved participants. The subtype of ROLE/INVOLVED relation in verb-noun pairs also includes: Involved-agent/ patient/ instrument/location/ direction Involved-source-direction Involved-target-direction Examples like write, V and pencil, N are in INVOLVE-INSTRUMENT relation in that the meaning of the verb write incorporates the meaning of the noun pencil. The relations are expressed as follows:. (13). {write, V} INVOLVED INSTRUMENT {pencil, N}. (14). {hammer, N} ROLE INSTRUMENT {to hammer, V}. 2.2.3. Other Relations of Verbs. From WordNet or EuroWordNet’s classification, verb entries related to each other by their paradigmatic relations such as hyponymy, antonym and meronymy. However, this kind of classification may easily organized verbs into a flat or shallow hierarchy of classes [9]. Verbs can also relate with each other in a syntagmatic way. Syntagmatic relations constrain the contexts in which a word may be used, and can be seen as a complementary way of representing speaker’s lexical knowledge [17] [11]. For example, Chklovski and Pantel in [9] proposed a finer-grained analysis of verb relations, which explicitly breaks out the temporal precedence between entities. Verbs which are related with one another syntagmatically together made up a broad-coverage semantic network called VerbOcean1 . VerbOcean identified five semantic relations between 1. http://demo.patrickpantel.com/Content/verbocean/. 20.

(31) verbs. This can be summarized in Table 2.1 with a comparison to WordNet. Semantic relation. Example. Alignment with WordNet Symmetric. similarity. transform / integrate. synonyms or siblings. Y. strength. push / nudge. synonyms or siblings. N. antonym. open / close. antonymy. Y. enablement. wash / clean. cause. N. happens-before. buy / have; marry / divorce cause; entailment. N. Table 2.1: A finer-grained semantic relation among verbs. [9]. Similarity Very similar to the synonym in WordNet, verbs are often similar or related but not one hundred percent equals to each other. Similarity occurs between action verbs, for example, transform/ integrate or produce/create.. Strength As mentioned, verbs can be very similar to one another but differ in their strength. When two verbs are similar, one of them may denote a stronger intention, more absolute in action like verb pairs push vs. nudge. Or in the case of change-of-state verbs, one may carry a more complete change than the other, such as wound vs. kill.. Antonym Also similar to the classification in WordNet, antonymy is also known as semantic opposition. As Fellbaum described, it can arise from switching the thematic roles of verb pairs like buy vs. sell, give and take. Antonym can also occurred in stative verbs like live vs. die. 21.

(32) Enablement Enablement can be seen as a type of causal relation hold between two verbs and satisfy the formula that V1 is accomplished by V2 , examples are like accomplish/ complete, and fight/win.. Happens-before The relation between two verbs may exhibit temporally disjoint intervals. The happens-before relation hold between two verbs corresponds to the Cause relation in WordNet which shows no temporal inclusion of two verbs but indicates two events happens in order such as sell happens before buy, and marry happens before divorce.. To sum up, semantic relations among verbs can be organized in terms of paradigmatic and syntagmatic perspectives. Paradigmatic organization relates words in hierarchy, that is, connect a more basic word to a more general concept or superordinate. Lexical resources like WordNet, EuroWordNet and many dictionaries identify paradigmatic relations among synsets or lexical entries. Some lexicons also supply syntagmatic relations between the target and other words by means of illustrative sentences. However, there is no consistency in the classification of semantic relations of verbs. In this section, we introduced semantic relations of verbs in WordNet, EuroWordNet and VerbOcean. The comparison of verb relations in these three databases can be summarized by Table 2.2.. 2.3. Troponymy. Section 2.2 has introduced semantic relations of verbs, this section turns the focus back to the target relation in this thesis—the hypernymy/troponymy relation. Our literature survey reveals that, to the best of our knowledge, there is no study targeting at the troponymy extraction yet. 22.

(33) WordNet. EuroWordNet. VerbOcean. Synonym. NEAR SYNONYM. Similarity. Synset (verb groups). XPOS NEAR SYN. Produce:: create. {Adore, V:: adornment, N}. Hypernymy/Troponymy. HYPERNYM/HYPONYM. Strength. Walk:: limp. XPOS HYPERNYM/HYPONYM. Wound:: kill. {love, V}::{emotion, N}. Antonym. ANTONYM. Antonym. {close, shut}::{open}. XPOS ANTONYM. Open:: close. {live, V}::{dead, A}. Cause. CAUSE (XPOS). Enablement. Kill::{die, decease, perish. . . }. {kill, V}::{die, V}. Fight:: win. {kill, V}::{death, N}. Entailment. SUBEVENT(XPOS). Happens-before. {divorce, split up}:: {marry, wed. . . }. snore, V::sleep, V. marry:: divorce. but, N::payment, N. ROLE/INVOLVED {write, V}::{pencil, N} {teach, V}::{teacher, N}. Table 2.2: Semantic relations of verbs in Wordnet, EuroWordNet and VerbOcean. 23.

(34) Troponymy relation is discussed less often than other semantic relations. In fact, this term was first dubbed and defined by Fellbaum and Miller in [31]. Therefore, in this section, I will try my best to cover a detailed introduction of troponymy basically based on Fellbaum et al.’s studies [15] [19] [16] [17] [31].. 2.3.1. Definition of Troponymy. Basically, troponymy can be seen as the hypernymy-hyponymy relation of verbs. While troponymy is the term used to indicate the relation, troponym, on the other hand, is used to describe the subordinate word in this relation. For example, verb pair move and walk are in hypernymy/troponymy relation while move is the hypernym of walk and walk is the troponym of move. Hypernymy/troponymy relation, by its definition, may be corresponded to the is-a relation of nouns, but unlike nouns, verbs do not straightforward fit into the is-a relation nor related in a consistent mode, rather, verbs are connected by a manner elaboration from some superordinate verbs. As mentioned, entailment lies under all verbal relations thus the concept of entailment is important for troponymy to hold since troponymy is a special kind of entailment. Saying that troponymy is a kind of entailment indicates that a troponym inherits and entails its superordinate verb but is specified in different manner. Basing on this assumption, the definition of troponymy can be expressed by the formula: to V1 is to V2 in some particular manner. So, walking describes a manner of moving, elaborating on the speed and style. Therefore we say that walk is a troponym of move and move is the hypernym of walk. Similar to the is-a relation among nouns, troponymy also builds hierarchical structures with the semantically most inclusive verb at the root and increasingly specified subordinate verbs as the extending branches and leaves. However, unlike noun hierarchies which are tall and deep, verb hierarchies tend to be ‘flatter’ and ‘bushy’, most of them do not exceed more than three or four levels.. 24.

(35) As mentioned, there are four kinds of lexical entailments of verb relations, demarcated by two features {± temporal inclusion, ± co-extensiveness}. Temporal inclusion distinct verb pairs succeed/try from pairs like snore/ sleep and move/walk since the former pair demonstrates two temporally disjoint events while the latter indicate proper inclusion in time. But what criterion makes verb pair snore/sleep different from move/walk for that snore in not a troponym of sleep but walk is a troponym of move? According to Fellbaum and Miller [19], saying that troponymy is a particular kind of entailment involves temporal co-extensiveness for the two verbs. That is, the proposition of a troponym must and always entail its superordinate event occurs at the same time. For example, although snore entails sleep and is temporally included in sleep, we can not say that snore is a troponym of sleep, they are not in a hypernymy/ troponymy relation. This pair of verbs is related only by entailment and proper temporal inclusion. The important generalization here is that verbs related by entailment and proper temporal inclusion cannot be related by troponymy. On the contrary, for troponymy to hold—for we can say that to V1 is to V2 in some specific manner, the essential factor is the co-extensiveness in time: one can sleep before or after snoring, but not necessarily happened at the same time. Take another hypernymy/troponymy verb pair limp and walk for example, limping entails walking and walking can be said to be a part of limping, they are temporally coextensive, that is, limp and walk happen at the same time.. Beside the complicated distinction among verbs themselves, the troponymy relation is also different from the is-a relation among nouns in two ways [17]. First, the is-a-kind-of formula linking semantic related nouns may cause oddness when applying to verbs. For example, ‘(to) yodel is a kind of (to) sing.’ sounds odd only when changing into gerund form ‘yodeling is a. 25.

(36) kind of singing’ will make it acceptable. Second, in the case of nouns, kind of can be omitted without changing the truth statement, for instance, ‘A donkey is a kind of animal.’ equals ‘A donkey is an animal.’ By contrast, the same deletion makes verbs odd as the following sentences show: ‘Murmuring is talking/ To murmur is to talk.’ These differences indicate that there is more than just a is-a relation among concepts expressed by verbs and the way that used to distinct nouns and adjectives is not the same as the way we distinct verbs. To sum up, troponymy links verbs in a manner elaboration rather than kind relation.. 2.3.2. Distinguishing Manner. The most important factors of troponymy are manner elaboration and co-extensiveness as introduced above. However, the so-called ‘manner’, in fact, can be further analyzed and distinguished. Fellbaum [31] examined a few areas of the verb lexicon where the encoding of a manner component has interesting consequences, and attempted to draw some distinctions among different kinds of troponymy.. Function vs. Manner Many nouns that fit into the is-a-kind-of relation do not necessarily in hypernymy/hyponymy relation. Pustejovsky [40] discussed the semantic meaning of nouns like pet which he calls roles. Take dog and pet for example, dog can be a kind of pet, but the relation between dog/animal and pet/animal are different, the intrinsic meaning of these two words are not the same. There is no problem to state that a dog is a kind of animal but it is odd to say that a pet is a kind of dog/animal. The reason is that ‘pet’ is the role that an animal plays in a certain setting. 26.

(37) or under certain condition which may be culturally and temporarily dependent. On the contrary, the is-a relation that links dog to its superordinate animal is biologically stable. The same phenomenon exists in the verb lexicon. Many verbs may have meaning aspects that depend on the context. For example, run, walk, and swim are all manners of motion verbs move. On the other hand, run, walk, swim, along with bike, row or ski are also manners of exercise. Similar to the role function of nouns, exercise is an unstable superordinate verb because the activities that can be defined as exercise may vary. But move is a stable hypernym of run, walk, swim, etc. because it inherits its meaning to the subordinate verbs. Therefore, the relation between run, walk, swim, etc. and exercise is being called the function relation. The distinction between genuine troponymy and the function relation is reflected in the fact that in cases like the above, exercise is not a part of these verbs’ meanings in the same way that their superordinate move is.. Result vs. Manner Besides verbs expressing the function or manner in which an action is carried out, English has many verbs that encode the result of an action but not the manner of achieving this result. For example, a result verb like shut does not carry the manner of the motion by itself, but depends on the noun object that denotes the entity that is acted upon. To make it clear, in the case of shut, the manner of shutting a window is definitely different from the manner of shutting a book. The manner is not specified by shut itself but by the noun object following shut. In contrast to result verbs, a manner of motion verb like run denotes a specific kind of traveling motion that does not vary depending on who or what does the running. For this reason, the result verbs are also called accomplishment verbs, and manner verbs are activity verbs.. Perhaps the most significant relation among lexicon semantics is the hypernymy/hyponymy. 27.

(38) relation, which builds hierarchical structures. Troponymy, as this relation is called in the verb lexicon, apparently relates verbs in terms of a manner elaboration. The so-called ‘manner’, according to Fellbaum, can be further analyzed and distinguished and thus yields different types of troponymy. Table 2.3 summarized three different types of troponymy. Manner verb. Function verb. Result verb. The event itself is carried The verb encodes the function. The verb encodes only the. out by the verb.. that is context- or situation-dependent. result of an action.. e.g. move::run. e.g. exercise:: run. e.g. shut:: snap. Table 2.3: Three different types of Troponymy. Fellbaum introduced a very detailed analysis of entailment in [16] [18] and also the complication of troponymy [15]. The clear distinction and definition of hypernymy/troponymy relation made by Fellbaum therefore motivates this thesis and also serves as the norm in describing and evaluating the result.. 2.4. Automatic Discovery of Lexical Semantic Relation. Automated extraction of semantic resources has been widely studied by researchers. From Machine readable dictionaries [32] [41] to the unrestricted web corpus [22] [21] [9], Natural Language Processing researchers use these databases to develop algorithms for harvesting lexical semantic relations [21], observing rules [27], or extracting target words [20] [28]. Also, there has been a variety of studies focusing on the automatic acquisition of different lexical semantic relations, such as hypernymy/hyponymy [21], antonymy [28], meronymy [20] [7] and so on. Now I begin to discuss some approaches used in previous studies.. 28.

(39) 2.4.1. Lexico Syntactic Pattern–Based Approach. Automatically finding semantic relations among synsets by using pattern-based algorithm may be the most common approach [39]. It is generally agreed that the structure of a lexical entry in a dictionary sometimes reflects the relatedness of words and concepts [22]; also, certain structures or syntactic patterns usually define the semantic relation among each other. The following three subsections introduce some influential and novel studies on automated extraction.. Nakamura & Nagao (1988) and Hearst (1992) Early studies analyzed and extracted information from Machine Readable Dictionaries (MRD) (cf. [5] [33] [32] [30]). By syntactic or phrasal patterns found in definitions, certain semantic relations can be accessed automatically. For example, Nakamura and Nagao [32] created a Machine Readable Database (MRD) from Longman Dictionary of Contemporary English (LDOCE), by extracting key Verb/ key Noun which express a ‘key concepts’ of the defined verb, the algorithm can automatically extract the taxonomic information and certain semantic relations. Basing on the similar spirit, Hearst [21] pioneered enlarging the scope to the unrestricted text as her database. Similar to previous pattern-based interpretation techniques, she proposed the LSPE (Lexico-Syntactic Pattern Extraction) method, upon which most of the related works are based, to automatically find hyponym relations among nouns. Manually constructed three patterns at first, Hearst discovered three more new patterns from seed instances by bootstrapping. After the pattern-discovery procedure, six lexico-syntactic patterns which denote the concept of ‘including’ or ‘other than’ were postulated. And basing on a text corpus, which contains terms and expressions that are not defined in Machine Readable Dictionaries; the six lexico-syntactic patterns successfully detected hypernymy-hyponymy relation and extracted these pairs from the sentences. The six syntactic patterns used in Hearst’s algorithm. 29.

(40) are as follows: (1) X such as Y; (2) such X as Y; (3) Y, or other X; (4) Y, and other X; (5) X, including Y; (6) X, especially Y. For terms that are present in the above patterns, this algorithm successfully captures the relation that Ys are hyponymy of Xs. Hearst’s pattern-based approach successfully extracted nouns which are in hypernym-hyponym relation from large unrestricted text. Inspired by Hearst, it is hoped that other lexical relations can also be acquirable in the same way, or at least, from the similar paradigm.. Ramanand and Bhattachayya (2008) Automated semantic relation discovering can be further used to evaluate the quality and the reliability of an existing thesaurus like Princeton WordNet. Ramanand and Bhattachayya [41] pioneered to propose an algorithm based on dictionary definitions to verify that synsets in WordNet are indeed synonymous. As mentioned in the previous section, WordNet resembles a thesaurus in that it represents word meanings primarily in terms of conceptual-semantic and lexical relation. Therefore, a synset, the foundation of WordNet, is constructed by assembling a set of synonyms that together define a unique sense. The basic idea of Ramanand and Bhattachayya’s approach is that a lexical entry in the dictionary is usually defined in terms of its hypernyms or synonyms. Hence, they use the concept of synset to suggest that ‘if a word w is present in a synset along with other words w1,w2,. . . wk , then there is a dictionary definition of w which refers to its hypernym or to its synonyms from the synset.’ With this assumption, three groups of rules were applied in order in the algorithm for validating synonymy and hypernymy relation among Wordnet synsets. For example, for the rule which can denote hypernymy, the author defined that the definitions of words for particular senses often make references to the hypernym of the concept. Such as synset: { brass, brass instrument} and its hypernym: {wind instrument, wind}. The relevant definition of brass instrument is: a musical wind instrument of. 30.

(41) brass or other metal with a cup-shaped mouthpiece, as the trombone, tuba, French horn, trumpet, or cornet. The positive results of Ramanand and Bhattachayya’s approach proved that the basic idea behind the algorithm holds well: by using the definitions of each word, hypernymous or synonymous relations among words can be discovered.. Chklovski & Pantel (2004) and other related works In [9], Chklovski and Pantel also used some simple lexico-syntactic patterns to automatically discover fine-grained verb semantics. The so-called ‘fine-grained’ semantics, as mentioned in section 2.2.3, relates words in a syntagmatic way. It is differed in focus from previous studies which related verbs to each other by organizing them into classes or identifying their frames or thematic role. It is also differed from WordNet which provides relations between verbs at a coarser level, such as hypernym, antonym, synonym, etc. Chklovski and Pantel related verbs in a broad-coverage semantics such as strength, enablement, and temporal information. Verbs which are related in a syntagmatical way together made up a semantic network called VerbOcean. Syntactic pattern -based approach were then proposed by Chklovski and Pantel in order to automatically extract verb relations within VerOcean. The approach can be divided into two stages. First step was to extract highly associated verb pairs as experimental data which were output by a paraphrasing algorithm called DIRT proposed in [27]. Afterwards, syntactic patterns were used to detect the possible semantic relations among verbs. By examining pairs of verbs in known semantic relation, 35 syntactic patterns were selected manually. For example, one of the surface patterns used to identify the strength relation is: X and ever Y; Yed or at least Xed, and for patterns that denote enablement are: Xed by Ying the or to X by Ying the, etc. Since verbs are the primary core for describing events and events are exhibiting the rela-. 31.

(42) tion of different entities, successfully identified fine-grained semantic relations is important in question-answering or summarization system.. 2.4.2. Clustering-Based Approach. Finding semantic relations by clustering is another approach used in automated extraction. However, it is less common and has been applied only on is-a relation insofar [39]. Recently, Pantel and Ravichandran [38] proposed an algorithm labeling semantic classes and their concepts by clustering and then automatically harvest is-a relation using a top-down approach. Unlike a bottom-up approach which depends on surface syntactic patterns to discover hyponym relations (cf. [21]), Pantel and Ravichandran’s is a top down approach. They made use of semantic classes which discovered by clustering algorithm called CBC (Clustering by Committee) proposed in [37] and output a ranked list of concept names for each semantic class. After knowing the general concept of a group of words, the extraction of hyponyms will be easy. For example, the following semantic class extracted automatically by CBC consists of words shown below: (A) multiple sclerosis, diabetes, osteoporosis, cardiovascular, disease, Parkinson’s, rheumatoid arthritis, heart disease, asthma, cancer, hypertension, lupus, high blood pressure, arthritis, emphysema, epilepsy, cystic fibrosis, leukemia, hemophilia, Alzheimer, myeloma, glaucoma, schizophrenia, .. After the above semantic class (A) is labeled with the concept disease by using its member’s lexical or syntactic dependencies, the is-a relationship can be extracted easily such as: multiple sclerosis is a disease, and diabetes is a disease, etc.. 32.

(43) 2.4.3. Bootstrapping Approach. The rich and structured semantic information in WordNet or EuroWordNet can be transported through accurate translation if the conceptual relations defined by lexical semantic relations (Hereafter, LSRs) remain constant in both languages. In Huang et al.’s paper [24] [25], crosslingual LSR inferences were examined by bootstrapping a Chinese Wordnet with Princeton WordNet. As known, a semantic network is critical for knowledge processing, but it is not an easy task to create a semantic-relation based wordnet by manually construction. Therefore, by using an existing wordnet as a medium, it is hoped that a wordnet for a low-density language can be built through bootstrapping. The basic idea of bootstrapping lies in the universality of lexical semantic relations; through accurate translation between two languages, an existing database such as WordNet can serve as a potential common semantic network. In [24], a small set experiment data including 210 most frequently used Chinese words were served as input data and examined the validity of cross-lingual LSR inferences by bootstrapping a Chinese Wordnet from Princeton WordNet.. Figure 2.3: Translation-mediated LSR Prediction (The complete model). Figure 2.3 can demonstrate the bootstrapping method in Huang et al.’s study: CW1 represents. 33.

(44) the starting Chinese lemma which can be linked to EW1 through the translation relation i, and therefore EW1 can provide a set of LSRs based on WordNet. EW2 , under this situation, can be linked with EW1 through relation x. LSR prediction is mapped back to Chinese when EW2 is translated to CW2 . However, according to the authors, Figure 2.3 is only an ideal bootstrapping model since language translation involves more than semantic correspondences. Social and cultural factors also play a role in human choices of translation equivalents. Regardless this fact, the aim of Huang et al.’s study is to see how much lexical semantic information is inferable across different languages so the translational idiosyncrasies are ignored and the ideal model can be illustrated in Figure 2.4 where it is assumed that there is no translational discrepancy between CW1 / EW1 and CW2 / EW2 .. Figure 2.4: Translation-mediated LSR Prediction (when translation equivalents are synonymous). Hypernymy, hyponymy and antonym relation are tested in [24] for these relations are transitive and allow clear logical predictions when combined. The results show that lexical semantic relation translations are indeed highly precise when they are logically inferable. Also, in terms of syntactic categories, it is observed that nominal semantic relations are more reliably inferred 34.

(45) cross-linguistically than verbal semantic relations. The fact that verbal relations are less reliable after bootstrapping is not a surprising result since polysemous lemmas have lower possibility of being synonymous to the corresponding English synset. That is to say, verb meanings are more mutable than noun meanings hence make it more difficult in cross lingual bootstrapping. In sum, the positive results in Huang et al.’s study set up a theoretical model for the following researches that LSRs do have the ability to transport cross-lingually, at least for hypernymy, hyponymy, and antonym relations.. 2.5. Summary. In this chapter, the studies pertaining to the following issues are reviewed: semantic relationbased Wordnets, lexical semantic relations of verbs, troponymy, and automated approaches of extracting semantic relations. Creating a semantic relation-based database is crucial for knowledge mining and natural language processing. Some semantic relations such as is-a relation, part-of relation have been successfully discovered by automated method. However, the literature survey reveals that there is no study targeting at hypernymy-troponymy relation in Chinese yet. Therefore, in this chapter, detailed definition and information of troponymy are being reviewed; also, three approaches on automatic extraction of semantic relations are introduced as well. To date, most researches on lexical relation discovering have been focused on is-a relation or part-of relation by using syntactic pattern-based or clustering-based approach. Syntactic pattern-based algorithm is a bottom-up approach and is the most commonly used one for that sentence structures can always denote relations and reveal information among lexical entries. On the other hand, the top-down clustering-based approach, which is used less often, has an advantage that it can identify hyponymous relations that do not explicitly appear in text or patterns. Bootstrapping 35.

(46) approach has been used for mapping English WordNet onto Chinese Wordnet but the experimental data is rather small and an important factor–translational idiosyncrasy– is being ignored. The literature review shows that none of these approaches were used to extract troponymy relation particularly in any languages: syntactic-based approach is used to extract is-a relation and part-of relation only among nouns, and although clustering-based approach has been used to extract relations of verbs, it focused on the syntagmatic relation but not hierarchical one. This thesis, therefore, is going to adopt the syntactic pattern-based and bootstrapping approach for extracting semantic relations. Clustering-based approach, unfortunately, is rather unachievable during this phase so will not be concerned as one of the approaches. It is hoped that the results can serve as a useful framework for the subsequent studies on the extraction of semantic relations.. 36.

(47) Chapter 3 Methodology This chapter presents two main approaches that this thesis adopts to automatically extract Chinese verb pairs in hypernymy-troponymy relation. These two approaches were implemented parallely and will be illustrated separately in the following sections. Section 3.1 describes how lexical syntactic pattern-based approach processes while Section 3.2 illustrates the procedure of bootstrapping approach. Finally, how to evaluate and score our results will be introduced in the final part of this chapter.. 3.1. Syntactic Pattern-Based Approach. This section introduces the first approach used in the thesis, including the data source, two syntactic patterns in Chinese, the programming language and the procedure.. 3.1.1. Database: Chinese WordNet. In this thesis, all verbs in Chinese WordNet (CWN) were used as the experimental data. Launched by Academia Sinica, CWN can be seen as a machine readable dictionary which covers 5600 medium frequency words and their explicit senses (until 2006). In recent years, 37.

(48) more and more studies on automatic extraction rely on large corpus or the unrestricted Web text as mining resources since the web serves as a strong application of data mining. The information on the web is immense, free, and easily available; it contains hundreds of billions of words or text, and can be used for all manners of language research. Despite the multi-faceted advantages of using the Web as corpus, it is more feasible for our approach to narrow down the scope to a dictionary-like database—- Chinese WordNet for the following considerations: First, situation in Chinese is much more complicated in unrestricted text. Due to the varied forms and contents of the unrestricted text, language is easily metaphorized in free text. Take the is-a relation from web for example, we searched the corresponding pattern in Chinese ShiYiZhong ‘is a kind of’ from the search engine Google1 and the following sentences are the first five results returned by it:. (1). 馬爺爺04：筷筷子是一種槓槓桿 (Chopsticks is a kind of lever.). (2). 她說寫寫作是一種治治療- WRETCH (Writing is a kind of treatment.). (3). 博客來書籍館跆跆拳是一種態態度 (Tae-kwon-do is a kind of attitude.). (4). 「勾勾心鬥角」是一種能能力？(Intriguing against each other is a kind of ability.). (5). 天下文化書坊亂亂是一種新新商機 (Chaos is a kind of commercial possibility.). From the above example sentences, it is apparent that none of the noun pairs which fix the N1 is a kind of N2 pattern are in hypernymy-hyponymy relation. At least, none of these pairs indicate the is-a relation in WordNet; rather, all of them are being metaphorized to express some abstract 1. http://www.google.com.tw/ (retrieved on 2008/11/5). 38.

(49) concepts. The first five results returned by the searching engine were not in accordance with our taxonomic aim, implying that they might have lowered the precision rate because of the large amount of indirect and unrelated data on the web. For this reason, unrestricted web texts are not considered to serve as the mining resource in this study. Second, in this thesis we proposed two syntactic patterns which can indicate hypernymy-troponymy relation. We assume that there might exist a lexical semantic relation between a defined entry and its dictionary definition, represented through certain syntactic patterns. In this thesis, it is postulated that the hypernym of the defined verb might appear in the definition. Therefore a dictionary-like database with explicit definition of each sense like CWN was chosen. Third, for a WordNet-like database in Chinese such as CWN, lexical semantic relations between senses or lemmas themselves have not been fully constructed yet. Hence, it is hoped that the results returned by our approaches can be applied directly onto the existing database, helping to enhance its integrity.. 3.1.2. Data Pre-processing. Verbs with distinct senses were extracted from CWN word list, serving as the experimental data in the approach. This input verb list contains 10388 entries of verb senses in total and each verb entry is followed by its definition. As known, most verbs have more than one sense, and each sense of a given word might have their own hypernyms and troponyms. Note that the verb list extracted from CWN contains verbs with distinct senses, indicating that each entry represents one sense but not one verb; also, different senses of a same verb were marked by serial numbers. Next, for the sake of programming, we apply the following preprocessing methods to the source data:. • Tokenizing: Tokenization was applied onto the input text, aiming at separating punctua39.