Abstract
This paper presents a novel approach to character identification, that is an entity linking task that maps mentions to characters in dialogues from TV show transcripts. We first augment and correct several cases of annotation errors in an existing corpus so the corpus is clearer and cleaner for statistical learning. We also introduce the agglomerative convolutional neural network that takes groups of features and learns mention and mention-pair embeddings for coreference resolution. We then propose another neural model that employs the embeddings learned and creates cluster embeddings for entity linking. Our coreference resolution model shows comparable results to other state-of-the-art systems. Our entity linking model significantly outperforms the previous work, showing the F1 score of 86.76% and the accuracy of 95.30% for character identification.- Anthology ID:
- K17-1023
- Volume:
- Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)
- Month:
- August
- Year:
- 2017
- Address:
- Vancouver, Canada
- Venue:
- CoNLL
- SIG:
- SIGNLL
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 216–225
- Language:
- URL:
- https://aclanthology.org/K17-1023
- DOI:
- 10.18653/v1/K17-1023
- Cite (ACL):
- Henry Y. Chen, Ethan Zhou, and Jinho D. Choi. 2017. Robust Coreference Resolution and Entity Linking on Dialogues: Character Identification on TV Show Transcripts. In Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017), pages 216–225, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Robust Coreference Resolution and Entity Linking on Dialogues: Character Identification on TV Show Transcripts (Chen et al., CoNLL 2017)
- PDF:
- https://preview.aclanthology.org/nodalida-main-page/K17-1023.pdf