Congratulations Varada!! On Tue, 28 Jul 2009, Ted Pedersen wrote:
> 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 > > >
