Label Embedding for Zero shot Fine grained Named Entity Typing
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.
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