@inproceedings{stratos-2017-entity,
title = "Entity Identification as Multitasking",
author = "Stratos, Karl",
editor = "Chang, Kai-Wei and
Chang, Ming-Wei and
Srikumar, Vivek and
Rush, Alexander M.",
booktitle = "Proceedings of the 2nd Workshop on Structured Prediction for Natural Language Processing",
month = sep,
year = "2017",
address = "Copenhagen, Denmark",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-4302/",
doi = "10.18653/v1/W17-4302",
pages = "7--11",
abstract = "Standard approaches in entity identification hard-code boundary detection and type prediction into labels and perform Viterbi. This has two disadvantages: 1. the runtime complexity grows quadratically in the number of types, and 2. there is no natural segment-level representation. In this paper, we propose a neural architecture that addresses these disadvantages. We frame the problem as multitasking, separating boundary detection and type prediction but optimizing them jointly. Despite its simplicity, this architecture performs competitively with fully structured models such as BiLSTM-CRFs while scaling linearly in the number of types. Furthermore, by construction, the model induces type-disambiguating embeddings of predicted mentions."
}
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%0 Conference Proceedings
%T Entity Identification as Multitasking
%A Stratos, Karl
%Y Chang, Kai-Wei
%Y Chang, Ming-Wei
%Y Srikumar, Vivek
%Y Rush, Alexander M.
%S Proceedings of the 2nd Workshop on Structured Prediction for Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F stratos-2017-entity
%X Standard approaches in entity identification hard-code boundary detection and type prediction into labels and perform Viterbi. This has two disadvantages: 1. the runtime complexity grows quadratically in the number of types, and 2. there is no natural segment-level representation. In this paper, we propose a neural architecture that addresses these disadvantages. We frame the problem as multitasking, separating boundary detection and type prediction but optimizing them jointly. Despite its simplicity, this architecture performs competitively with fully structured models such as BiLSTM-CRFs while scaling linearly in the number of types. Furthermore, by construction, the model induces type-disambiguating embeddings of predicted mentions.
%R 10.18653/v1/W17-4302
%U https://aclanthology.org/W17-4302/
%U https://doi.org/10.18653/v1/W17-4302
%P 7-11
Markdown (Informal)
[Entity Identification as Multitasking](https://aclanthology.org/W17-4302/) (Stratos, 2017)
ACL
- Karl Stratos. 2017. Entity Identification as Multitasking. In Proceedings of the 2nd Workshop on Structured Prediction for Natural Language Processing, pages 7–11, Copenhagen, Denmark. Association for Computational Linguistics.