@inproceedings{3cceba97017443e0acb5af09868812ba,
title = "Multi-source named entity typing for social media",
abstract = "Typed lexicons that encode knowledge about the semantic types of an entity name, e.g., that 'Paris' denotes a geolocation, product, or person, have proven useful for many text processing tasks. While lexicons may be derived from large-scale knowledge bases (KBs), KBs are inherently imperfect, in particular they lack coverage with respect to long tail entity names. We infer the types of a given entity name using multi-source learning, considering information obtained by alignment to the Freebase knowledge base, Web-scale distributional patterns, and global semi-structured contexts retrieved by means of Web search. Evaluation in the challenging domain of social media shows that multi-source learning improves performance compared with rule-based KB lookups, boosting typing results for some semantic categories.",
author = "Reuth Vexler and Einat Minkov",
note = "Publisher Copyright: {\textcopyright} Proceedings of NEWS 2016: 6th Named Entity Workshop at the 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016. All rights reserved.; 6th Named Entity Workshop, NEWS 2016 at the 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016 ; Conference date: 12-08-2016",
year = "2016",
doi = "10.18653/v1/w16-2702",
language = "American English",
series = "Proceedings of NEWS 2016: 6th Named Entity Workshop at the 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016",
publisher = "Association for Computational Linguistics (ACL)",
pages = "11--20",
editor = "Xiangyu Duan and Banchs, {Rafael E.} and Min Zhang and Haizhou Li and A. Kumara",
booktitle = "Proceedings of NEWS 2016",
address = "United States",
}