Automatic Labeling of Hypernymy- Troponymy Relation for Chinese Verbs

• Main Authors: Chiao-Shan, Lo, 羅巧珊
• Other Authors: Shu-Kai, Hsieh
• Format: Others
• Language: en_US
• Published: 2009
• Online Access: http://ndltd.ncl.edu.tw/handle/cbw5yx

碩士 === 國立臺灣師範大學 === 英語學系 === 97 === 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.