Abstract
This paper describes our contribution to the SemEval 2016 Workshop. We participated in the Shared Task 8 on Meaning Representation parsing using a transition-based approach, which builds upon the system of Wang et al. (2015a) and Wang et al. (2015b), with additions that utilize a Feedforward Neural Network classifier and an enriched feature set. We observed that exploiting Neural Networks in Meaning Representation parsing is challenging and we could not benefit from it, while the feature enhancements yielded an improved performance over the baseline model.
| Original language | English |
|---|---|
| Title of host publication | SemEval 2016 - 10th International Workshop on Semantic Evaluation, Proceedings |
| Publisher | Association for Computational Linguistics (ACL) |
| Pages | 1154-1159 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781941643952 |
| Publication status | Published - 2016 |
| Externally published | Yes |
| Event | 10th International Workshop on Semantic Evaluation, SemEval 2016 - San Diego, United States Duration: 2016 Jun 16 → 2016 Jun 17 |
Publication series
| Name | SemEval 2016 - 10th International Workshop on Semantic Evaluation, Proceedings |
|---|
Conference
| Conference | 10th International Workshop on Semantic Evaluation, SemEval 2016 |
|---|---|
| Country | United States |
| City | San Diego |
| Period | 16/6/16 → 16/6/17 |
ASJC Scopus subject areas
- Theoretical Computer Science
- Computational Theory and Mathematics
- Computer Science Applications
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Cite this
Puzikov, Y., Kawahara, D., & Kurohashi, S. (2016). M2L at SemEval-2016 task 8: AMR parsing with neural networks. In SemEval 2016 - 10th International Workshop on Semantic Evaluation, Proceedings (pp. 1154-1159). (SemEval 2016 - 10th International Workshop on Semantic Evaluation, Proceedings). Association for Computational Linguistics (ACL).