By Ian C. Chow, Jonathan J. Webster (auth.), Alexander Gelbukh (eds.)
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Extra resources for Computational Linguistics and Intelligent Text Processing: 8th International Conference, CICLing 2007, Mexico City, Mexico, February 18-24, 2007. Proceedings
We can notice that concept hypernyms hi belonging to a deﬁnition are also used in the similarity calculus. But we only take into account hypernyms distant at most of three edges from a concept hierarchy root. The English EuroWordNet’s deﬁnitions are sometimes concise and the addition to them of some hypernyms, close to the concepts (excepted those too general), helps to determine the correct Wikipedia’s deﬁnition which often contains terms related to these hypernyms. 4 Results Analysis We perform our process on 1000 EuroWordNet’s concepts which are present in the English and French database.
Each regular expression is defined with a related predicate. If a sentence is matched with one of the regular expression patterns, arguments are extracted from the pattern, and a concept is generated with the arguments and the pattern-related predicate. Fig. 4 shows examples of translation and concept generation by using regular extractions. (2) When there is no matched expression pattern, a subject and a predicate of a sentence are extracted by using parse tree information. Then, we remove unnecessary words such as ‘대부분의(almost all)’, ‘어떤(some, certain)’ from each subject and predicate that were extracted, and create a concept with the remained part.
Finally, our process for enriching a thesaurus is not dependant of a particular language. It can be extended to other European languages which do not have a large coverage of relationships and no deﬁnitions related to concepts in their own thesaurus. References 1. org/wiki/. (2006) 2. : Indexing with wordnet synsets can improve text retrieval. In: Proceeding of the workshop on Usage of Wordnet for NLP, ACL-98. (1998) 38–44 3. : Wordnet: an online lexical database. In: International journal of lexicography.