Title :
Word-Sense Disambiguation using maximum entropy model
Author :
Chatterjee, Niladri ; Misra, Rohit
Author_Institution :
Dept. of Math., Indian Inst. of Technol. Delhi, New Delhi, India
Abstract :
Natural languages are typically replete with homographs, words which have more than one meaning. Consequently, machine understanding of natural language sentences sometimes suffers from certain ambiguities in getting the correct sense of a word in a given sentence. In this work we present a trainable model for word sense disambiguation (WSD) for resolving this ambiguity. The proposed model applies concepts of information theory to find the appropriate sense of a word when the context is known. Given a training text tagged with the correct senses of a particular word, our model learns to classify each occurrence of the target word with its correct sense in the unseen text.
Keywords :
maximum entropy methods; natural language processing; homographs; information theory; maximum entropy model; natural languages; word sense disambiguation; Computer science; Context modeling; Dictionaries; Entropy; Frequency; Humans; Information theory; Marine animals; Mathematics; Natural languages; Information Theory; Natural Language Processing; Word Sense Disambiguation;
Conference_Titel :
Methods and Models in Computer Science, 2009. ICM2CS 2009. Proceeding of International Conference on
Conference_Location :
Delhi
Print_ISBN :
978-1-4244-5051-0
DOI :
10.1109/ICM2CS.2009.5397973