By Alexander Gelbukh
This publication constitutes the refereed lawsuits of the eighth overseas convention on Computational Linguistics and clever textual content Processing, CICLing 2007, held in Mexico urban, Mexico in February 2007.
The fifty three revised complete papers provided including three invited papers have been rigorously reviewed and chosen from 179 submissions. The papers conceal all present concerns in computational linguistics study and current clever textual content processing purposes. The papers are prepared in topical sections on: lexical assets, corpus-based wisdom acquisition, morphology and part-of-speech tagging, named entity popularity, temporal expression remedy, be aware segmentation, sentence splitting, chunking, grammar formalisms and syntax, notice experience disambiguation and discrimination, semantics, humor and emotion research; desktop translation, ordinary language iteration, clever tutoring structures, info retrieval, query answering, textual content summarization and knowledge extraction, textual content categorization and clustering, and spell-checking.
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Additional info for Computational Linguistics and Intelligent Text Processing: 8th International Conference, CICLing 2007, Mexico City, Mexico, February 18-24, 2007, Proceedings ... Computer Science and General Issues)
4%. 1 Introduction This paper describes a hybrid English-Korean Machine Translation (E-K MT) method for making a Korean ConceptNet (K-ConceptNet) based on English ConceptNet . ConceptNet is an easily usable, freely available commonsense knowledge base and natural-language-processing toolkit which supports many practical textual-reasoning tasks over real-world documents including topic-gisting, affect-sensing, analogymaking, and other context-oriented inferences. 6 million assertions of commonsense knowledge covering the spatial, physical, social, temporal, and psychological aspects of everyday life.
Because OpenMind sentences have words not found in dictionaries (we call them “out-of-vocabulary (OOV) words”), which are usually broken words or newly coined words, they have been handled through our auto-correction word list (pairs of frequently occurring typos and their correct expressions) and a Web dictionary. Since the corpus is in a highly structured short sentence form, and the first sense among the senses of an ambiguous word is correct, we hypothesized that we would have relatively clean translations compared to other texts such as news paper, novel, etc.
This is one more reason why we do not want to trust the unknown language identification methods of search engines. 40 G. Grefenstette gather about 5 million unique URLs that cover the entire French lexicon as a basis for creating our language model. 3 Extracting Language Modeling Information Once we are convinced that we have enough text for the language, and we have its full list of words (surface forms), we can begin to build the model of the language as shown in the previous section. While collecting information about the frequency of each word, we can also collect a seed list of URLs for each word.
Computational Linguistics and Intelligent Text Processing: 8th International Conference, CICLing 2007, Mexico City, Mexico, February 18-24, 2007, Proceedings ... Computer Science and General Issues) by Alexander Gelbukh