Entity Identification as Multitasking

Karl Stratos


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.
Anthology ID:
W17-4302
Volume:
Proceedings of the 2nd Workshop on Structured Prediction for Natural Language Processing
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Editors:
Kai-Wei Chang, Ming-Wei Chang, Vivek Srikumar, Alexander M. Rush
Venue:
WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
7–11
Language:
URL:
https://aclanthology.org/W17-4302
DOI:
10.18653/v1/W17-4302
Bibkey:
Cite (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.
Cite (Informal):
Entity Identification as Multitasking (Stratos, 2017)
Copy Citation:
PDF:
https://aclanthology.org/W17-4302.pdf
Code
 karlstratos/mention2vec
Data
CoNLL 2003