On the use of automatically acquired examples for all-nouns Word Sense Disambiguation
David Martinez, Oier Lopez de Lacalle, Eneko Agirre
JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH | AI ACCESS FOUNDATION | Published : 2008
This article focuses on Word Sense Disambiguation (WSD), which is a Natural Language Processing task that is thought to be important for many Language Technology applications, such as Information Retrieval, Information Extraction, or Machine Translation. One of the main issues preventing the deployment of WSD technology is the lack of training examples for Machine Learning systems, also known as the Knowledge Acquisition Bottleneck. A method which has been shown to work for small samples of words is the automatic acquisition of examples. We have previously shown that one of the most promising example acquisition methods scales up and produces a freely available database of 150 million exampl..View full abstract
Awarded by the Ministry of Education
Awarded by the Basque Government
Awarded by the Australian Research Council
This work has been partially financed by the Ministry of Education (KNOW project, ICT-2007-211423) and the Basque Government (consolidated research groups grant, IT-397-07). Oier Lopez de Lacalle was supported by a PhD grant from the Basque Government. David Martinez was funded by the Australian Research Council, grant no. DP0663879.