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» Taxonomy Learning Using Word Sense Induction
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AAAI
2006
13 years 8 months ago
Kernel Methods for Word Sense Disambiguation and Acronym Expansion
The scarcity of manually labeled data for supervised machine learning methods presents a significant limitation on their ability to acquire knowledge. The use of kernels in Suppor...
Mahesh Joshi, Ted Pedersen, Richard Maclin, Sergue...
BMCBI
2010
186views more  BMCBI 2010»
13 years 6 months ago
Knowledge-based biomedical word sense disambiguation: comparison of approaches
Background: Word sense disambiguation (WSD) algorithms attempt to select the proper sense of ambiguous terms in text. Resources like the UMLS provide a reference thesaurus to be u...
Antonio Jimeno Yepes, Alan R. Aronson
ACL
1996
13 years 8 months ago
Integrating Multiple Knowledge Sources to Disambiguate Word Sense: An Exemplar-Based Approach
In this paper, we present a new approach for word sense disambiguation (WSD) using an exemplar-based learning algorithm. This approach integrates a diverse set of knowledge source...
Hwee Tou Ng, Hian Beng Lee
ACL
2010
13 years 4 months ago
Unsupervised Ontology Induction from Text
Extracting knowledge from unstructured text is a long-standing goal of NLP. Although learning approaches to many of its subtasks have been developed (e.g., parsing, taxonomy induc...
Hoifung Poon, Pedro Domingos
ACL
2003
13 years 8 months ago
Exploiting Parallel Texts for Word Sense Disambiguation: An Empirical Study
A central problem of word sense disambiguation (WSD) is the lack of manually sense-tagged data required for supervised learning. In this paper, we evaluate an approach to automati...
Hwee Tou Ng, Bin Wang, Yee Seng Chan