Abstract
Event detection is an important NLP task that has been only recently tackled in the context of Polish, mostly due to lack of language resources. The available annotated corpora are still relatively small and supervised learning approaches are limited by the size of training datasets. Event detection tools are very much needed, as they can be used to annotate more language resources automatically and to improve the accuracy of other NLP tasks, which rely on the detection of events, such as question answering or machine translation. In this paper we present a deep learning based approach to this task, which proved to capture the knowledge contained in the training data most effectively and outperform previously proposed methods. We show a direct comparison to previously published results, using the same data and experimental setup.- Anthology ID:
- 2019.gwc-1.27
- Volume:
- Proceedings of the 10th Global Wordnet Conference
- Month:
- July
- Year:
- 2019
- Address:
- Wroclaw, Poland
- Editors:
- Piek Vossen, Christiane Fellbaum
- Venue:
- GWC
- SIG:
- SIGLEX
- Publisher:
- Global Wordnet Association
- Note:
- Pages:
- 216–221
- Language:
- URL:
- https://aclanthology.org/2019.gwc-1.27
- DOI:
- Cite (ACL):
- Łukasz Kobyliński and Michał Wasiluk. 2019. Deep Learning in Event Detection in Polish. In Proceedings of the 10th Global Wordnet Conference, pages 216–221, Wroclaw, Poland. Global Wordnet Association.
- Cite (Informal):
- Deep Learning in Event Detection in Polish (Kobyliński & Wasiluk, GWC 2019)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/2019.gwc-1.27.pdf