Identifying Motion Entities in Natural Language and A Case Study for Named Entity Recognition

Ngoc Phuoc An Vo, Irene Manotas, Vadim Sheinin, Octavian Popescu


Abstract
Motion recognition is one of the basic cognitive capabilities of many life forms, however, detecting and understanding motion in text is not a trivial task. In addition, identifying motion entities in natural language is not only challenging but also beneficial for a better natural language understanding. In this paper, we present a Motion Entity Tagging (MET) model to identify entities in motion in a text using the Literal-Motion-in-Text (LiMiT) dataset for training and evaluating the model. Then we propose a new method to split clauses and phrases from complex and long motion sentences to improve the performance of our MET model. We also present results showing that motion features, in particular, entity in motion benefits the Named-Entity Recognition (NER) task. Finally, we present an analysis for the special co-occurrence relation between the person category in NER and animate entities in motion, which significantly improves the classification performance for the person category in NER.
Anthology ID:
2020.coling-main.460
Volume:
Proceedings of the 28th International Conference on Computational Linguistics
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
5250–5258
Language:
URL:
https://aclanthology.org/2020.coling-main.460
DOI:
10.18653/v1/2020.coling-main.460
Bibkey:
Cite (ACL):
Ngoc Phuoc An Vo, Irene Manotas, Vadim Sheinin, and Octavian Popescu. 2020. Identifying Motion Entities in Natural Language and A Case Study for Named Entity Recognition. In Proceedings of the 28th International Conference on Computational Linguistics, pages 5250–5258, Barcelona, Spain (Online). International Committee on Computational Linguistics.
Cite (Informal):
Identifying Motion Entities in Natural Language and A Case Study for Named Entity Recognition (Vo et al., COLING 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.coling-main.460.pdf
Data
CoNLL-2003SICK