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Supertagging for Domain Adaptation: An Approach with Law Texts


Sugisaki, Kyoko (2017). Supertagging for Domain Adaptation: An Approach with Law Texts. In: The 16th International Conference on Artificial Intelligence and Law, London, 12 June 2017 - 16 June 2017.

Abstract

In this paper, we present a German supertagger that analy- ses syntactic functions in linear order. We apply a statistical sequential model, conditional random fields (CRF), to Swiss law texts, in a real world scenario in which the training data of the domain is missing. We show that the small amount of in-domain training data that was informed by linguistic hard and soft constraints and domain constraints achieved a label accuracy of 90% in the domain data, thus outperforming state-of-the-art parsers.

Abstract

In this paper, we present a German supertagger that analy- ses syntactic functions in linear order. We apply a statistical sequential model, conditional random fields (CRF), to Swiss law texts, in a real world scenario in which the training data of the domain is missing. We show that the small amount of in-domain training data that was informed by linguistic hard and soft constraints and domain constraints achieved a label accuracy of 90% in the domain data, thus outperforming state-of-the-art parsers.

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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:16 June 2017
Deposited On:06 Jun 2017 09:07
Last Modified:28 Jul 2018 06:29
Funders:Swiss National Science Foundation
OA Status:Green
Project Information:
  • : FunderSNSF
  • : Grant ID
  • : Project TitleSwiss National Science Foundation

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