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        <title>Label Embedding for Zero-shot Fine-grained Named Entity Typing</title>
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            <namePart type="given">Yuji</namePart>
            <namePart type="family">Matsumoto</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T Label Embedding for Zero-shot Fine-grained Named Entity Typing
%A Ma, Yukun
%A Cambria, Erik
%A Gao, Sa
%Y Matsumoto, Yuji
%Y Prasad, Rashmi
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F ma-etal-2016-label
%X 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.
%U https://aclanthology.org/C16-1017
%P 171-180

Markdown (Informal)

[Label Embedding for Zero-shot Fine-grained Named Entity Typing](https://aclanthology.org/C16-1017) (Ma et al., COLING 2016)

• Label Embedding for Zero-shot Fine-grained Named Entity Typing (Ma et al., COLING 2016)

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.