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Combining unsupervised and supervised methods for PP attachment disambiguation


Volk, M (2002). Combining unsupervised and supervised methods for PP attachment disambiguation. In: COLING-2002, Taipeh, 2002 - 2002.

Abstract

Statistical methods for PP attachment fall into two classes according to the training material used: first, unsupervised methods trained on raw text corpora and second, supervised methods trained on manually disambiguated examples. Usually supervised methods win over unsupervised methods with regard to attachment accuracy. But what if only small sets of manu-
ally disambiguated material are available? We show that in this case it is advantageous to intertwine unsupervised and supervised methods into one disambiguation algorithm that outperforms both methods used alone.

Statistical methods for PP attachment fall into two classes according to the training material used: first, unsupervised methods trained on raw text corpora and second, supervised methods trained on manually disambiguated examples. Usually supervised methods win over unsupervised methods with regard to attachment accuracy. But what if only small sets of manu-
ally disambiguated material are available? We show that in this case it is advantageous to intertwine unsupervised and supervised methods into one disambiguation algorithm that outperforms both methods used alone.

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Additional indexing

Item Type:Conference or Workshop Item (Paper), refereed, original work
Communities & Collections:06 Faculty of Arts > Institute of Computational Linguistics
Dewey Decimal Classification:000 Computer science, knowledge & systems
410 Linguistics
Language:English
Event End Date:2002
Deposited On:24 Aug 2009 12:50
Last Modified:05 Apr 2016 13:19
Permanent URL: http://doi.org/10.5167/uzh-20338

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