Abstract
In this paper, we propose to use a set of simple, uniform in architecture LSTM-based models to recover different kinds of temporal relations from text. Using the shortest dependency path between entities as input, the same architecture is used to extract intra-sentence, cross-sentence, and document creation time relations. A “double-checking” technique reverses entity pairs in classification, boosting the recall of positive cases and reducing misclassifications between opposite classes. An efficient pruning algorithm resolves conflicts globally. Evaluated on QA-TempEval (SemEval2015 Task 5), our proposed technique outperforms state-of-the-art methods by a large margin. We also conduct intrinsic evaluation and post state-of-the-art results on Timebank-Dense.- Anthology ID:
- D17-1092
- Volume:
- Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
- Month:
- September
- Year:
- 2017
- Address:
- Copenhagen, Denmark
- Editors:
- Martha Palmer, Rebecca Hwa, Sebastian Riedel
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 887–896
- Language:
- URL:
- https://aclanthology.org/D17-1092
- DOI:
- 10.18653/v1/D17-1092
- Cite (ACL):
- Yuanliang Meng, Anna Rumshisky, and Alexey Romanov. 2017. Temporal Information Extraction for Question Answering Using Syntactic Dependencies in an LSTM-based Architecture. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 887–896, Copenhagen, Denmark. Association for Computational Linguistics.
- Cite (Informal):
- Temporal Information Extraction for Question Answering Using Syntactic Dependencies in an LSTM-based Architecture (Meng et al., EMNLP 2017)
- PDF:
- https://preview.aclanthology.org/naacl24-info/D17-1092.pdf