Abstract
Named entity typing is the task of detecting the types of a named entity in context. For instance, given “Eric is giving a presentation”, our goal is to infer that ‘Eric’ is a speaker or a presenter and a person. Existing approaches to named entity typing cannot work with a growing type set and fails to recognize entity mentions of unseen types. In this paper, we present a label embedding method that incorporates prototypical and hierarchical information to learn pre-trained label embeddings. In addition, we adapt a zero-shot learning framework that can predict both seen and previously unseen entity types. We perform evaluation on three benchmark datasets with two settings: 1) few-shots recognition where all types are covered by the training set; and 2) zero-shot recognition where fine-grained types are assumed absent from training set. Results show that prior knowledge encoded using our label embedding methods can significantly boost the performance of classification for both cases.- Anthology ID:
- C16-1017
- Volume:
- Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
- Month:
- December
- Year:
- 2016
- Address:
- Osaka, Japan
- Editors:
- Yuji Matsumoto, Rashmi Prasad
- Venue:
- COLING
- SIG:
- Publisher:
- The COLING 2016 Organizing Committee
- Note:
- Pages:
- 171–180
- Language:
- URL:
- https://aclanthology.org/C16-1017
- DOI:
- Cite (ACL):
- Yukun Ma, Erik Cambria, and Sa Gao. 2016. Label Embedding for Zero-shot Fine-grained Named Entity Typing. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 171–180, Osaka, Japan. The COLING 2016 Organizing Committee.
- Cite (Informal):
- Label Embedding for Zero-shot Fine-grained Named Entity Typing (Ma et al., COLING 2016)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/C16-1017.pdf
- Data
- FIGER