المؤلفون: Shu-Kai Hsieh

المصدر: International Journal of Computer Processing of Languages. 23:243-253

مصطلحات موضوعية: Structure (mathematical logic), Metonymy, Information retrieval, Lexical semantics, business.industry, Computer science, InformationSystems_INFORMATIONSTORAGEANDRETRIEVAL, WordNet, Representation (arts), computer.software_genre, eXtended WordNet, Semantic memory, Artificial intelligence, Polysemy, business, computer, Natural language processing

الوصف: The representation of lexical semantic knowledge has been one of the most important research topics in the field of computational lexical semantics. Among relevant lexical resources, the design architecture of Princeton WordNet is the most popular one. In this paper, however, we argue that the current synset scheme requires more extensions when applied to the analysis of deeper sense structure in Chinese Wordnet. Issues involved include the underlying structure of sense, meaning facet and their relations. Based on a large amount of empirical analysis of sense data, this paper proposes a fine-grained framework in representing lexical semantic knowledge for Chinese Wordnet, which we believe will be an important consideration for the envisioned cross-lingual global wordnet grid construction. The systematic polysemy patterns found among meaning facets can also be used as a human gold standard of hand-annotated data for metonymy resolution task.

URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_________::b6025dc932976ad2c3d5e3852b14c0b5
https://doi.org/10.1142/s1793840611002334

المؤلفون: Shu-Kai Hsieh, Chu-Ren Huang, Petr Šimon, Jia-Fei Hong

المصدر: International Journal of Computer Processing of Languages. 21:19-30

مصطلحات موضوعية: Computer science, business.industry, InformationSystems_INFORMATIONSTORAGEANDRETRIEVAL, computer.software_genre, ComputingMethodologies_ARTIFICIALINTELLIGENCE, Sketch, Entity linking, ComputingMethodologies_PATTERNRECOGNITION, Named-entity recognition, Transliteration, Word sketch, Chinese language, Artificial intelligence, Chinese word, business, computer, Natural language processing

الوصف: One unique challenge in Chinese Language Processing is cross-strait named entity recognition. Due to the adoption of different transliteration strategies, foreign name transliterations can vary greatly between the PRC and Taiwan, creating difficulties in NLP tasks including data mining, translation and information retrieval. In this paper, we introduce a novel approach to automatic extraction of divergent transliterations of foreign named entities that bootstraps co-occurrence statistics from tagged Chinese corpora, thereby producing higher precision.

URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_________::997c2e42542b59d4eac5e5e9b41545bf
https://doi.org/10.1142/s1793840608001780