Abstract
The current leading paradigm for temporal information extraction from text consists of three phases: (1) recognition of events and temporal expressions, (2) recognition of temporal relations among them, and (3) time-line construction from the temporal relations. In contrast to the first two phases, the last phase, time-line construction, received little attention and is the focus of this work. In this paper, we propose a new method to construct a linear time-line from a set of (extracted) temporal relations. But more importantly, we propose a novel paradigm in which we directly predict start and end-points for events from the text, constituting a time-line without going through the intermediate step of prediction of temporal relations as in earlier work. Within this paradigm, we propose two models that predict in linear complexity, and a new training loss using TimeML-style annotations, yielding promising results.- Anthology ID:
- D18-1155
- Volume:
- Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
- Month:
- October-November
- Year:
- 2018
- Address:
- Brussels, Belgium
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1237–1246
- Language:
- URL:
- https://aclanthology.org/D18-1155
- DOI:
- 10.18653/v1/D18-1155
- Cite (ACL):
- Artuur Leeuwenberg and Marie-Francine Moens. 2018. Temporal Information Extraction by Predicting Relative Time-lines. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1237–1246, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- Temporal Information Extraction by Predicting Relative Time-lines (Leeuwenberg & Moens, EMNLP 2018)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/D18-1155.pdf
- Code
- tuur/PredRelTimelines