http://lml.bas.bg/ranlp2011/invited.php#navigli
Summary: Word Sense Disambiguation (WSD), the task of automatically associating meaning with words in context, is a long-standing problem in the field of computational linguistics. There can be no doubt the problem is a tough one. Researchers began to study the automatic association of meanings with words as long ago as the late 1940s. And they have been struggling to put their ideas into effective practice ever since. All too frequently their results have been disappointing not only in terms of disambiguation quality, but also when their WSD has been plugged into applications such as Information Retrieval and Machine Translation. Nevertheless, this pessimistic scenario has been progressively changing over the last decade, to the point that high disambiguation performance has been reported in recent work on the topic, indicating that WSD is more than alive. In this talk I will "challenge" the skeptic and analyze how and why WSD has achieved remarkable improvements in the last few years, and what promises it holds for the near future in terms of both in vitro performance and end-to-end applications.
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