Publication: Supertagging for Domain Adaptation: An Approach with Law Texts
Supertagging for Domain Adaptation: An Approach with Law Texts
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Sugisaki, K. (2017, June 16). Supertagging for Domain Adaptation: An Approach with Law Texts. The 16th International Conference on Artificial Intelligence and Law, London. https://doi.org/10.1145/3086512.3086543
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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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- Sugisaki, Kyoko
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Sugisaki, K. (2017, June 16). Supertagging for Domain Adaptation: An Approach with Law Texts. The 16th International Conference on Artificial Intelligence and Law, London. https://doi.org/10.1145/3086512.3086543
