Toward Socially-Infused Information Extraction: Embedding Authors, Mentions, and Entities (original) (raw)


Anthology ID:

D16-1152

Volume:

Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

Month:

November

Year:

2016

Address:

Austin, Texas

Editors:

Jian Su,Kevin Duh,Xavier Carreras

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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PDF:

https://aclanthology.org/D16-1152.pdf

Attachment:

D16-1152.Attachment.pdf