Titlebook: Automatic Processing of Natural-Language Electronic Texts with NooJ; 9th International Co Tatsiana Okrut,Yuras Hetsevich,Hanna Stanislavenk

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Tatsiana Okrut,Yuras Hetsevich,Hanna StanislavenkaIncludes supplementary material:

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Communications in Computer and Information Sciencehttp://image.papertrans.cn/b/image/166438.jpg

DEAWL

Gail M. Ferguson,Marc H. Bornsteinto sections by different subject categories. From the broad list of possible subject categories in the sections the corpus focuses on fiction, historic, medical, scientific, sociological literature, etc. Given a great number of similar subject categories, the first one-million corpus can be consider

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TIA742

https://doi.org/10.1057/9781137439369 it describes. After having chosen ALGO ‘something’ as the head of our hierarchy, we distinguish between HECHO ‘fact’ and ENTIDAD ‘entity’, each of which are further partitioned into around twenty subclasses. Facts correspond to predicates while entities can correspond either to semantic names (obje

委派

George Jennings,Beatriz Cabrera Velázquezlevel of categorial ambiguity. Very often, the words belonging to these two parts of speech (PsOS) are homographs and semantically contiguous. Therefore, when necessary, and in most of their occurrences, it may be useful to automatically parse their correct linguistic functions. Essentially, this is

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通知

Anita Raj,Emma Jackson,Serena Dunhamjectives. The properties described in these tables enable the recognition and generation of adjectival constructions where the adjective has a predicative function. These properties also establish semantic relationships between adjective, noun and verb predicates, allowing new paraphrasing capabilit