Ontology-based word sense disambiguation using semi-automatically constructed ontology

Ontology-Based Word Sense Disambiguation by Using Semi-Automatically Constructed Ontology Sin-Jae Kang and Jong-Hyeok Lee Div. of Electrical and Computer Engineering, Pohang University of Science and Technology San 31 Hyoja-dong Nam-gu, Pohang 790-784 Republic of KOREA {sjkang, jhlee}@postech.ac.kr Abstract This paper describes a method for disambiguating word senses by using semi-automatically constructed ontology. The ontology stores rich semantic constraints among 1,110 concepts, and enables a natural language processing system to resolve semantic ambiguities by making inferences with the concept network of the ontology. In order to acquire a reasonably practical ontology in limited time and with less manpower, we extend the existing Kadokawa thesaurus by inserting additional semantic relations into its hierarchy, which are classified as case relations and other semantic relations. The former can be obtained by converting valency information and case frames from previously-built electronic dictionaries used in machine translation. The latter can be acquired from concept co- occurrence information, which is extracted automatically from large corpora. In our practical machine translation system, our word sense disambiguation method achieved a 9.2% improvement over methods which do not use an ontology for Korean translation. Keywords word sense disambiguation, ontology construction, corpus analysis, concept association 1 Introduction An ontology is a knowledge base with information about concepts existing in the world or domain, their properties, and how they relate to each other. Three principal reasons to use an ontology in machine translation (MT) are to enable source language analyzers and target language generators to share knowledge, to store semantic constraints, and to resolve semantic ambiguities by making inferences with the concept network of the ontology (Mahesh, 1996; Nirenburg et al., 1992). An ontology is different from a thesaurus in that it contains only language independent information and many other semantic relations, as well as taxonomic relations. In this paper, we propose to use the ontology to disambiguate word senses. All approaches to word sense disambiguation (WSD) make use of words in a sentence to mutually disambiguate each other. The distinctions between various approaches lie in the source and type of knowledge made by the lexical units in a sentence. Our WSD approach is a hybrid method, which combines the advantages of corpus-based and knowledge-based methods. We use the ontology as an external knowledge source and secured dictionary information as context information. First, we apply the previously-secured dictionary information to select the correct senses of some ambiguous words with high precision, and then use the ontology to disambiguate the remaining ambiguous words. In this paper, a semi-automatic LIP (language independent and practical) ontology construction method is also proposed briefly. In order to acquire reasonably practical ontology in limited time and with less manpower, we take full advantage of already existing knowledge resources and practical usages in corpora. First, we introduced the same number and grain size of concepts of the Kadokawa thesaurus (Ohno & Hamanishi, 1981) and its taxonomic hierarchy into the ontology. The second strategy is to extend the hierarchy of the Kadokawa thesaurus by inserting additional semantic relations into its …

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