Neural Semantic Role Labeling with Dependency Path Embeddings

ACL 2016  ·  Michael Roth, Mirella Lapata ·

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 sub-sequences 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.

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Datasets


Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Chinese Semantic Role Labeling CoNLL-2009 Roth and Lapata (2016) F1 75.3 # 3
Precision 76.9 # 3
Recall 73.8 # 3

Methods


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