Difference between revisions of "Lexical Acquisition"

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Most deep lexical resources have been developed manually by lexicographers. Manual work is costly and the resulting resources have limited coverage, and require labour-intensive porting to new tasks. Automatic lexical acquisition is a more promising and cost-effective approach to take, and is increasingly viable given recent advances in NLP and machine learning technology, and corpus availability.
 
Most deep lexical resources have been developed manually by lexicographers. Manual work is costly and the resulting resources have limited coverage, and require labour-intensive porting to new tasks. Automatic lexical acquisition is a more promising and cost-effective approach to take, and is increasingly viable given recent advances in NLP and machine learning technology, and corpus availability.
  
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Latest revision as of 04:15, 25 June 2012

In natural language processing (NLP), there is a pressing need to develop deep lexical resources (e.g. lexicons for linguistically-precise grammars, template sets for information extraction systems, ontologies for word sense disambiguation). Such resources are critical for enhancing the performance of systems and for improving their portability between domains. For example, to perform reliably, an information extraction system needs access to high-quality lexicons or templates specific to the task at hand.

Most deep lexical resources have been developed manually by lexicographers. Manual work is costly and the resulting resources have limited coverage, and require labour-intensive porting to new tasks. Automatic lexical acquisition is a more promising and cost-effective approach to take, and is increasingly viable given recent advances in NLP and machine learning technology, and corpus availability.