Computer Science – Computation and Language
Scientific paper
2004-07-29
Proceedings of the Third International Workshop on the Evaluation of Systems for the Semantic Analysis of Text (SENSEVAL-3), (
Computer Science
Computation and Language
related work available at http://purl.org/peter.turney/
Scientific paper
This paper describes the National Research Council (NRC) Word Sense Disambiguation (WSD) system, as applied to the English Lexical Sample (ELS) task in Senseval-3. The NRC system approaches WSD as a classical supervised machine learning problem, using familiar tools such as the Weka machine learning software and Brill's rule-based part-of-speech tagger. Head words are represented as feature vectors with several hundred features. Approximately half of the features are syntactic and the other half are semantic. The main novelty in the system is the method for generating the semantic features, based on word \hbox{co-occurrence} probabilities. The probabilities are estimated using the Waterloo MultiText System with a corpus of about one terabyte of unlabeled text, collected by a web crawler.
No associations
LandOfFree
Word Sense Disambiguation by Web Mining for Word Co-occurrence Probabilities does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Word Sense Disambiguation by Web Mining for Word Co-occurrence Probabilities, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Word Sense Disambiguation by Web Mining for Word Co-occurrence Probabilities will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-457172