Lexical Acquisition - Revision history

Pdturney: Reverted edits by Creek (talk) to last revision by Kenlit

2012-06-25T11:15:17Z

Reverted edits by Creek (talk) to last revision by Kenlit

← Older revision		Revision as of 11:15, 25 June 2012
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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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Creek at 08:26, 25 June 2012

2012-06-25T08:26:52Z

← Older revision		Revision as of 08:26, 25 June 2012
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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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Kenlit at 16:29, 24 December 2007

2007-12-24T16:29:36Z

New page

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.

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