Disambiguating News Data
A Real-Time Disambiguation System
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In recent years, great advances have been made in the speed, accuracy, and coverage of automatic word sense disambiguators - systems that given a word appearing in a certain context, can identify the sense of that word. Word Sense Disambiguation (WSD) is traditionally considered an AI-hard problem, that is, a problem which can be solved only by first resolving all the difficult problems in artificial intelligence (AI), such as the representation of common sense and encyclopedic knowledge. Moreover, it has been found that people are inconsistent when asked to disambiguate words and this causes ...
In recent years, great advances have been made in the speed, accuracy, and coverage of automatic word sense disambiguators - systems that given a word appearing in a certain context, can identify the sense of that word. Word Sense Disambiguation (WSD) is traditionally considered an AI-hard problem, that is, a problem which can be solved only by first resolving all the difficult problems in artificial intelligence (AI), such as the representation of common sense and encyclopedic knowledge. Moreover, it has been found that people are inconsistent when asked to disambiguate words and this causes problems when testing the output of an automatic disambiguator. A breakthrough in this field would have a significant impact on many relevant web-base applications, such as information retrieval and information extraction. In the review of disambiguation research, many varied techniques for performing automatic disambiguation are introduced. In this book, three different language independentstrategies for word sense disambiguation are proposed and evaluated. The performances of the resulting system have been compared with respect to other well know methods for word sense disambiguation.
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