Work

Neural Semantic Role Labeling with Dependency Path Embeddings

Year: 2016

Type: conference-paper

Abstract: This paper introduces a novel model for semantic role labeling that makes use of neural sequence modeling techniques.Our approach is motivated by the observation that complex syntactic structures and related phenomena, such as nested subordinations and nominal predicates, are not handled well by existing models.Our model treats such instances as subsequences of lexicalized dependency paths and learns suitable embedding representations.We experimentally demonstrate that such embeddings can improve results over previous state-of-the-art semantic role labelers, and showcase qualitative improvements obtained by our method.

Source: Annual Meeting of the Association for Computational Linguistics (ACL)

DOI: 10.18653/v1/p16-1113

Authors: Michael Roth, Mirella Lapata

Institution: University of Edinburgh

Language: English

Topic: Natural Language Processing Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funders: Deutsche Forschungsgemeinschaft, H2020 European Research Council

Award: Translating from Multiple Modalities into Text