Toward Socially-Infused Information Extraction: Embedding Authors, Mentions, and Entities

Yi Yang, Ming-Wei Chang, Jacob Eisenstein


Anthology ID:
D16-1152
Volume:
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing
Month:
November
Year:
2016
Address:
Austin, Texas
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
1452–1461
Language:
URL:
https://aclanthology.org/D16-1152
DOI:
10.18653/v1/D16-1152
Bibkey:
Cite (ACL):
Yi Yang, Ming-Wei Chang, and Jacob Eisenstein. 2016. Toward Socially-Infused Information Extraction: Embedding Authors, Mentions, and Entities. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 1452–1461, Austin, Texas. Association for Computational Linguistics.
Cite (Informal):
Toward Socially-Infused Information Extraction: Embedding Authors, Mentions, and Entities (Yang et al., EMNLP 2016)
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