Hartmann, Silvana ; Mújdricza-Maydt, Éva ; Kuznetsov, Ilia ; Gurevych, Iryna ; Frank, Anette (2017)
Assessing SRL Frameworks with Automatic Training Data Expansion.
Valencia, Spain
Conference or Workshop Item, Bibliographie
Abstract
We present the first experiment-based study that explicitly contrasts the three major semantic role labeling frameworks. As a prerequisite, we create a dataset labeled with parallel FrameNet-, PropBank-, and VerbNet-style labels for German. We train a state-of-the-art SRL tool for German for the different annotation styles and provide a comparative analysis across frameworks. We further explore the behavior of the frameworks with automatic training data generation. VerbNet provides larger semantic expressivity than PropBank, and we find that its generalization capacity approaches PropBank in SRL training, but it benefits less from training data expansion than the sparse-data affected FrameNet.
| Item Type: | Conference or Workshop Item |
|---|---|
| Erschienen: | 2017 |
| Creators: | Hartmann, Silvana ; Mújdricza-Maydt, Éva ; Kuznetsov, Ilia ; Gurevych, Iryna ; Frank, Anette |
| Type of entry: | Bibliographie |
| Title: | Assessing SRL Frameworks with Automatic Training Data Expansion |
| Language: | English |
| Date: | April 2017 |
| Publisher: | Association for Computational Linguistics |
| Book Title: | Proceedings of the 11th Linguistics Annotation Workshop (LAW XI) at EACL 2017 |
| Event Location: | Valencia, Spain |
| URL / URN: | https://aclweb.org/anthology/W/W17/W17-0814.pdf |
| Abstract: | We present the first experiment-based study that explicitly contrasts the three major semantic role labeling frameworks. As a prerequisite, we create a dataset labeled with parallel FrameNet-, PropBank-, and VerbNet-style labels for German. We train a state-of-the-art SRL tool for German for the different annotation styles and provide a comparative analysis across frameworks. We further explore the behavior of the frameworks with automatic training data generation. VerbNet provides larger semantic expressivity than PropBank, and we find that its generalization capacity approaches PropBank in SRL training, but it benefits less from training data expansion than the sparse-data affected FrameNet. |
| Uncontrolled Keywords: | UKP_reviewed;Natural Language Processing;UKP_p_QAEduInf;UKP_p_CLARIND |
| Identification Number: | TUD-CS-2017-0045 |
| Divisions: | 20 Department of Computer Science 20 Department of Computer Science > Ubiquitous Knowledge Processing DFG-Graduiertenkollegs DFG-Graduiertenkollegs > Research Training Group 1994 Adaptive Preparation of Information from Heterogeneous Sources |
| Date Deposited: | 23 Feb 2017 11:35 |
| Last Modified: | 24 Jan 2020 12:03 |
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