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An N-gram language model aims at capturing statistical word order dependency information from corpora. Although the concept of language models has been applied extensively to handle a variety of NLP problems with reasonable success, the standard model does not incorporate semantic information, and consequently limits its applicability to semantic problems such as word sense disambiguation. We propose a framework that integrates semantic information into the language model schema, allowing adoi:10.1007/978-3-540-78135-6_24 dblp:conf/cicling/LinV08 fatcat:schjwheorbdefbc4dupg7ntuti