Greetings all, Varada will have her MS thesis defense today! The final version of her thesis will be available shortly, and I'm sure those of you with an interest in all words sense disambiguation will find it particularly interesting. I'll post a link to that when available.
Enjoy, Ted ============================================================================= COMPUTER SCIENCE COLLOQUIUM An Extended Analysis of a Method of All Words Sense Disambiguation Varada Kolhatkar Computer Science Graduate Student Tuesday, July 28, 2009 3:00 p.m. HELLER HALL 306 ABSTRACT One of the central problems in processing a natural language is ambiguity. In every natural language there are many potentially ambiguous words. Humans are fairly adept at solving ambiguity by drawing on context and their knowledge of the world. However, it is not so easy for machines to understand the intended meaning of a word in a given context. Word Sense Disambiguation (WSD) is the process of selecting the correct sense of a word in a specific context. It is often useful to generalize the problem of disambiguating a single word to that of disambiguating all content words in a given text. This generalized problem is referred to as ‘all-words sense disambiguation’. The long history of WSD research includes many different supervised, unsupervised and knowledge-based approaches. But the reality is that current state-of-the-art accuracy in WSD remains a long way off far from natural human abilities. This thesis presents our analysis of some of the components that might be contributing to the level of error currently plaguing all-words sense disambiguation. Our analysis makes use of WordNet::SenseRelate::AllWords, an unsupervised knowledge-based system for all-words sense disambiguation, which is freely available on the Web as a perl Module. The system assigns a WordNet sense to each word in a text using measures of semantic similarity and relatedness. We find that the degree of difficulty in disambiguating a word is proportional to the number of senses of that word (polysemy) which confirms the conclusion of Daelemans[10]. The experimental evidence indicates that a significant percentage of word sense disambiguation error is caused by a relatively small number of highly frequent word types. We also demonstrate that part-of-speech tagged text will be disambiguated more accurately than raw text. We show that expanding the context window helps in terms of coverage but doesn’t improve disambiguation. Finally we find that if the answer is not the most frequent sense, disambiguation turns out to be a hard problem even for an unsupervised system which doesn’t use any information about sense distribution. -- Ted Pedersen http://www.d.umn.edu/~tpederse ------------------------------------ Yahoo! Groups Links <*> To visit your group on the web, go to: http://groups.yahoo.com/group/nlpatumd/ <*> Your email settings: Individual Email | Traditional <*> To change settings online go to: http://groups.yahoo.com/group/nlpatumd/join (Yahoo! ID required) <*> To change settings via email: mailto:[email protected] mailto:[email protected] <*> To unsubscribe from this group, send an email to: [email protected] <*> Your use of Yahoo! Groups is subject to: http://docs.yahoo.com/info/terms/
