会议专题

An Experiment of Word Sense Disambiguation in a Machine Translation System

In this paper, we demonstrate an experiment of a machine translation (MT) system for two different languages, English and Persian. We also describe a model for word sense disambiguation (WSD) task inside the MT system, which uses decision trees automatically learned from a training data set, as its disambiguation formalism. Our evaluations can be divided into two different categories: evaluation on the whole MT system and evaluation on the WSD component. The experiments on the whole MT, shows that this system gets 16% with respect to NIST measure, while the evaluation on WSD using a corpus contains 860 aligned sentences shows that this component disambiguates 81.4% of ambiguous word correctly.

Machine Translation Word Sense Disambiguation Persian Language

Heshaam FAILI

Department of ECE,University of Tehran Tehran,Iran,

国际会议

The 2008 IEEE International Conference on Natural Language Processing and Knowledge Engineering(IEEE NLP-KE 2008)(2008IEEE自然语言处理与知识工程国际会议)

北京

英文

2008-10-19(万方平台首次上网日期,不代表论文的发表时间)