A Data-Driven Approach for Semantic Role Labeling from Induced Grammar Structures in Language

Vivek Datla, King-Ip (David) Lin, Max Louwerse, Abhinav Vishnu

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

    Abstract

    Semantic roles play an important role in extracting knowledge from text. Current unsupervised approaches utilize features from grammar structures, to induce semantic roles. The dependence on these grammars, however, makes it difficult to adapt to noisy and new languages. In this paper we develop a data-driven approach to identifying semantic roles, the approach is entirely unsupervised up to the point where rules need to be learned to identify the position the semantic role occurs. Specifically we develop a modified-ADIOS algorithm based on ADIOS Solan et al. (2005) to learn grammar structures, and use these grammar structures to learn the rules for identifying the semantic roles based on the context in which the grammar structures appeared. The results obtained are comparable with the current state-of-art models that are inherently dependent on human annotated data.
    Original languageEnglish
    Title of host publicationarXiv:1606.06274v1
    PublisherAAAI Press
    Publication statusPublished - 20 Jun 2016

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    Keywords

    • cs.CL

    Cite this

    Datla, V., Lin, K-I. D., Louwerse, M., & Vishnu, A. (2016). A Data-Driven Approach for Semantic Role Labeling from Induced Grammar Structures in Language. In arXiv:1606.06274v1 AAAI Press.