Automatic labeling of troponymy for Chinese verbs

Automatic labeling of troponymy for Chinese verbs

以同義詞集與詞彙語意關係架構而成的詞彙知識庫，如英語詞網（Wordnet）、歐語詞網（EuroWordnet）等，已有充分的研究，詞網的建構也已相當完善。基於相同的目的，中研院語言所亦已建立大規模之中文詞彙網路（Chinese Wordnet,CWN），旨在提供完整的中文辭彙之詞義區分。然而，在目前之中文詞彙網路系統中，由於目前主要是採用人為判定來標記同義詞集之間的語意關係，因此這些標記之數量尚未達成可行應用之一定規模。因此，本篇文章特別針對動詞之間的上下位詞彙語意關係（Troponymy），提出一種自動標記的方法。我們希望藉由句法上特定的句型（lexical syntactic pattern），建立一個能夠自動抽取出動詞上下位的系統。透過詞義意判定原則的評估，結果顯示，此系統自動抽取出的動詞上位詞，正確率將近百分之七十。本研究盼能將本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。

Synset and semantic relation based lexical knowledge base such as wordnet, have been well-studied and constructed in English and other European languages (EuroWordnet). The Chinese wordnet (CWN) has been launched by Academia Sinica basing on the similar paradigm. The synset that each word sense locates in CWN are manually labeled, however, the lexical semantic relations among synsets are not fully constructed yet. In this present paper, we try to propose a lexical pattern-based algorithm which can automatically discover the semantic relations among verbs, especially the troponymy relation. There are many ways that the structure of a language can indicate the meaning of lexical items. For Chinese verbs, we identify two sets of lexical syntactic patterns denoting the concept of hypernymy-troponymy relation. We describe a method for discovering these syntactic patterns and automatically extracting the target verbs and their corresponding hypernyms. Our system achieves satisfactory results and we beleive it will shed light on the task of automatic acquisition of Chinese lexical semantic relations and ontology learning as well.