Embedding Methods for Fine Grained Entity Type Classification

Dani Yogatama, Daniel Gillick, Nevena Lazic
2015 Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)  
We propose a new approach to the task of fine grained entity type classifications based on label embeddings that allows for information sharing among related labels. Specifically, we learn an embedding for each label and each feature such that labels which frequently co-occur are close in the embedded space. We show that it outperforms state-of-the-art methods on two fine grained entity-classification benchmarks and that the model can exploit the finer-grained labels to improve classification of standard coarse types.
doi:10.3115/v1/p15-2048 dblp:conf/acl/YogatamaGL15 fatcat:ct6ay6kjqvgxtcxuiot6tzsnw4