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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Entity LinkingEntity TypingNamed Entity Recognition (NER)Question AnsweringSentiment AnalysisZero-Shot LearningSimilar Papers 제목 키워드 기반
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