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FGCN
2008
IEEE
130views Communications» more  FGCN 2008»
13 years 11 months ago
Word Sense Disambiguation Based on Bayes Model and Information Gain
Word sense disambiguation has always been a key problem in Natural Language Processing. In the paper, we use the method of Information Gain to calculate the weight of different po...
Zhengtao Yu, Bin Deng, Bo Hou, Lu Han, Jianyi Guo
EMNLP
2007
13 years 6 months ago
Word Sense Disambiguation Incorporating Lexical and Structural Semantic Information
We present results that show that incorporating lexical and structural semantic information is effective for word sense disambiguation. We evaluated the method by using precise in...
Takaaki Tanaka, Francis Bond, Timothy Baldwin, San...
SAC
2005
ACM
13 years 10 months ago
A hierarchical naive Bayes mixture model for name disambiguation in author citations
Because of name variations, an author may have multiple names and multiple authors may share the same name. Such name ambiguity affects the performance of document retrieval, web ...
Hui Han, Wei Xu, Hongyuan Zha, C. Lee Giles
COLING
2002
13 years 4 months ago
A Maximum Entropy-based Word Sense Disambiguation System
In this paper, a supervised learning system of word sense disambiguation is presented. It is based on conditional maximum entropy models. This system acquires the linguistic knowl...
Armando Suárez, Manuel Palomar
EMNLP
2004
13 years 6 months ago
Unsupervised Domain Relevance Estimation for Word Sense Disambiguation
This paper presents Domain Relevance Estimation (DRE), a fully unsupervised text categorization technique based on the statistical estimation of the relevance of a text with respe...
Alfio Massimiliano Gliozzo, Bernardo Magnini, Carl...