What is word sense disambiguation good for?. Adam Kilgarriff. The goal of WSD research is usually taken to be disambiguation between senses given in a dictionary, thesaurus or similar. senses. Word senses are a response to constraints imposed by: tradition the printed page compactness
Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.
word sense disambiguation
The goal of WSD research is usually taken to be disambiguation between senses given in a dictionary, thesaurus or similar.
around the meaning.
I don't believe in word senses
and Croft (1992)
WSD program would improve performance
WS-ambiguity causes only limited degradation of IR performance
IR performance „with ambiguity“ and „without ambiguity“
introducing extra ambiguity did little to
the performance of [IR] systems is insensitive
to ambiguity but very sensitive to erroneous
(banana-kalashnikov) pretending to be a single word with 2 meanings
system performance with the disambiguation
module improved by up to 4.3%
4% is a significant improvement
No recent WSD work is employed in MT systems
Consider a case of
syntactic ambiguity (PP attachment):
1 I love baking cakes with friends.
2 I love baking cakes with butter icing.
Lexical information can resolve many syntactic ambiguities without being sense-disambiguated.
Does WS ambiguity cause problems for NLP applications?
IR: yes, to some moderate degree.
Problems can substantially be overcome by using longer queries.
MT: yes. Huge problem.
Addressed to date by lots and lots of selection restrictions.
Parsing: not known.
Lexicography: yes, WSD would be of benefit.
NLU: not much. NLU applications are mostly domain specic, and have some sort of domain model.