Computational Linguistics and Intelligent Text Processing: by Ian C. Chow, Jonathan J. Webster (auth.), Alexander Gelbukh

By Ian C. Chow, Jonathan J. Webster (auth.), Alexander Gelbukh (eds.)

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Additional resources for Computational Linguistics and Intelligent Text Processing: 8th International Conference, CICLing 2007, Mexico City, Mexico, February 18-24, 2007. Proceedings

Sample text

Furthermore, this information source, which moves every day, becomes a base of study for various scientific communities [14] [15]. When a request containing one word (simple or complex) is submitted to Wikipedia, the answer consists of a page which can be of various type: – If there is only one sense indexed in the encyclopedia relative to the submitted word, a page that we name definition page is shown. With the help of a suitable filtering of HTML code, we are able to recover the first paragraph of the page which can be considered as a definition of the word for a given sense.

Man has tried to describe the interesting and difficult in language. In the last part of the twentieth century, computers were added into the mix, exploiting large hand tagged text collections, first the Brown and Lancaster corpora, and finally the British National Corpus, with its 100 million words of hand corrected, part-of-speech tagged text. But even this computational linguistics research is ultimately based on the descriptions and choices made by the compilers of the hand tagged corpora. This first round of descriptive linguistics research has led to the creation of large language resources: large lexicons for morphological analysis, training text for part of speech taggers, and robust grammars for analyzing large quantities of text.

ETRI E-K Dictionary OpenMind Corpus ETRI K-E Dictionary E-ConcpetNet Corpus Concepts OOV Handling Triple Generation with Refinement Result1 Part III Korean Morphological Analysis Part II English POS Tagging (MontyTagger) ) MT S/W (EasyMan E/K) Part I E-K Machine Translation (Using a Commercial MT S/W) OpenMind English Raw Sentences Refinement Techniques (Concept Generalization, Web Dictionary Webster, Yahoo E-K Machine Translation (Using our Rule-based MT S/W) Combining Semiautomatically Result2 Generalization Building K-ConceptNet Fig.

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