mlsanityData to 7 Oct 2026

Neural Semantic Role Labeling with Dependency Path Embeddings

arXiv 1605.07515 ↗First posted 24 May 2016cs.CLcited by 15

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Abstract (arXiv)

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.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 May 2016.

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Citing papers per year: 2021: 7; 2022: 3; 2023: 2; 2024: 1; 2025: 0; 2026: 2.

15 papers.

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