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    Semantic role labelling with tree conditional random fields

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    Semantic Role Labelling with Tree Conditional Random Fields (63.04Kb)

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    Author
    COHN, TREVOR; Blunsom, Phil
    Date
    2005
    Source Title
    Proceedings, CoNLL-2005: Ninth Conference on Computational Natural Language
    University of Melbourne Author/s
    Cohn, Trevor; BLUNSOM, PHILIP CHARLES
    Affiliation
    Engineering: Department of Computer Science and Software Engineering
    Metadata
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    Document Type
    Conference Paper
    Citations
    Cohn, T., & Blunsom, P. (2005). Semantic role labelling with tree conditional random fields. In, Proceedings, CoNLL-2005: Ninth Conference on Computational Natural Language, Ann Arbor, Michigan.
    Access Status
    Open Access
    URI
    http://hdl.handle.net/11343/33833
    Abstract
    In this paper we apply conditional random fields (CRFs) to the semantic role labelling task. We define a random field over the structure of each sentence's syntactic parse tree. For each node of the tree, the model must predict a semantic role label, which is interpreted as the labelling for the corresponding syntactic constituent. We show how modelling the task as a tree labelling problem allows for the use of efficient CRF inference algorithms, while also increasing generalisation performance when compared to the equivalent maximum entropy classifier. We have participated in the CoNLL-2005 shared task closed challenge with full syntactic information.

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