Out-of-domain FrameNet Semantic Role Labeling
Silvana Hartmann, Ilia Kuznetsov, Teresa Martin, Iryna Gurevych
Abstract
Domain dependence of NLP systems is one of the major obstacles to their application in large-scale text analysis, also restricting the applicability of FrameNet semantic role labeling (SRL) systems. Yet, current FrameNet SRL systems are still only evaluated on a single in-domain test set. For the first time, we study the domain dependence of FrameNet SRL on a wide range of benchmark sets. We create a novel test set for FrameNet SRL based on user-generated web text and find that the major bottleneck for out-of-domain FrameNet SRL is the frame identification step. To address this problem, we develop a simple, yet efficient system based on distributed word representations. Our system closely approaches the state-of-the-art in-domain while outperforming the best available frame identification system out-of-domain. We publish our system and test data for research purposes.- Anthology ID:
- E17-1045
- Volume:
- Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers
- Month:
- April
- Year:
- 2017
- Address:
- Valencia, Spain
- Editors:
- Mirella Lapata, Phil Blunsom, Alexander Koller
- Venue:
- EACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 471–482
- Language:
- URL:
- https://aclanthology.org/E17-1045
- DOI:
- Cite (ACL):
- Silvana Hartmann, Ilia Kuznetsov, Teresa Martin, and Iryna Gurevych. 2017. Out-of-domain FrameNet Semantic Role Labeling. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pages 471–482, Valencia, Spain. Association for Computational Linguistics.
- Cite (Informal):
- Out-of-domain FrameNet Semantic Role Labeling (Hartmann et al., EACL 2017)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/E17-1045.pdf