Scaling to Very Very Large Corpora for Natural Language Disambiguation

Scaling to Very Very Large Corpora for Natural Language Disambiguation (2001)

by Michele Banko , Eric Brill

Citations:

82 - 3 self

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Datum	Value	Source
TITLE	Scaling to Very Very Large Corpora for Natural Language Disambiguation	user correction
AUTHOR NAME	Michele Banko	user correction
AUTHOR NAME	Eric Brill	user correction
ABSTRACT	The amount of readily available online text has reached hundreds of billions of words and continues to grow. Yet for most core natural language tasks, algorithms continue to be optimized, tested and compared after training on corpora consisting of only one million words or less. In this paper, we evaluate the performance of different learning methods on a prototypical natural language disambiguation task, confusion set disambiguation, when trained on orders of magnitude more labeled data than has previously been used. We are fortunate that for this particular application, correctly labeled training data is free. Since this will often not be the case, we examine methods for effectively exploiting very large corpora when labeled data comes at a cost.	user correction - Legacy Corrections
YEAR	2001	user correction - Legacy Corrections
CITATIONS	21 found	ParsCit 1.0