Automatic Semantic Classification for Chinese Unknown Compound Nouns

Automatic Semantic Classification for Chinese Unknown Compound Nouns Keh-Jiann Chen & Chao-jan Chen Institute of Information Science, Academia Sinica, Taipei Abstract The paper describes a similarity-based model to present the morphological rules for Chinese com- pound nouns. This representation model serves functions of 1) as the morphological rules of the compounds, 2) as a mean to evaluate the proper- ness of a compound construction, and 3) as a mean to disambiguate the semantic ambiguity of the morphological head of a compound noun. An automatic semantic classification system for Chine- se unknown compounds is thus implemented based on the model. Experiments and error analyses are also presented. 1. Introduction The occurrences of unknown words cause difficul- ties in natural language processing. The word set of a natural language is open-ended. There is no way of collecting every words of a language, since new words will be created for expressing new concepts, new inventions. Therefore how to identify new words in a text will be the most challenging task for natural language processing. It is especially true for Chinese. Each Chinese morpheme (usually a single character) carries meanings and most are polyse- mous. New words are easily constructed by com- bining morphemes and their meanings are the se- mantic composition of morpheme components. Of course there are exceptions of semantically non- compositional compounds. In Chinese text, there is no blank to mark word boundaries and no inflec- tional markers nor capitalization markers to denote the syntactic or semantic types of new words. Hence the unknown word identification for Chinese became one of the most difficult and demanding research topic. The syntactic and semantic categories of unknown words in principle can be determined by their content and contextual information. However many difficult problems have to be solved. First of all it is not possible to find a uniform representa- tional schema and categorization algorithm to han- dle different types of unknown words, since each type of unknown words has very much different morpho-syntactic structures. Second, the clues for identifying different type of unknown words are also different. For instance, identification of names of Chinese is very much relied on the surnames, which is a limited set of characters. The statistical methods are commonly used for identifying proper names (Chang et al. 1994, Sun et al. 1994). The identification of general compounds is more relied on the morphemes and the semantic relations between morphemes. There are co-occurrence restrictions between morphemes of compounds, but their relations are irregular and mostly due to common sense knowledge. The third difficulty is the problems of ambiguities, such as structure ambiguities, syntactic ambiguities and semantic ambiguities. For instances, usually a morpheme charac- ter/word has multiple meaning and syntactic categories. Therefore the ambiguity resolution became one of the major tasks. Compound nouns are the most frequently occurred unknown words in Chinese text. According to an inspection on the Sinica corpus (Chen etc. 1996), 3.51% of the word tokens in the corpus are unknown, i.e. they are not listed in the …

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