Title :
Towards Large-scale High-Performance English Verb Sense Disambiguation by Using Linguistically Motivated Features
Author :
Chen, Jinying ; Dligach, Dmitriy ; Palmer, Martha
Author_Institution :
BBN Technol., Cambridge
Abstract :
In this paper we describe the results of training high performance word sense disambiguation (WSD) systems on a new data set based on groupings of WordNet senses. This data set is designed to provide clear sense distinctions with sufficient examples in order to provide high quality training data. The sense distinctions are based on explicit syntactic and semantic criteria. Our WSD features utilize similar syntactic and semantic linguistic information. We demonstrate that this approach, using both maximum entropy and SVM models, produces systems whose performance is comparable to that of humans.
Keywords :
maximum entropy methods; natural language processing; support vector machines; word processing; SVM models; WordNet senses; large-scale high-performance English verb sense disambiguation; linguistically motivated features; maximum entropy; semantic linguistic information; syntactic linguistic information; word sense disambiguation; Computer science; Data mining; Entropy; High performance computing; Humans; Large-scale systems; Natural languages; Support vector machines; System performance; Training data;
Conference_Titel :
Semantic Computing, 2007. ICSC 2007. International Conference on
Conference_Location :
Irvine, CA
Print_ISBN :
978-0-7695-2997-4
DOI :
10.1109/ICSC.2007.69
