| DOI | Resolve DOI: https://doi.org/10.3115/v1/N15-1075 |
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| Author | Search for: Cherry, Colin1; Search for: Guo, Hongyu1 |
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| Affiliation | - National Research Council of Canada. Information and Communication Technologies
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| Format | Text, Article |
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| Conference | 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, May 31-June 5,2015, Denver, Colorado, USA |
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| Abstract | Named entity recognition (NER) systems trained on newswire perform very badly when tested on Twitter. Signals that were reliable in copy-edited text disappear almost entirely in Twitter’s informal chatter, requiring the construction of specialized models. Using well understood techniques, we set out to improve Twitter NER performance when given a small set of annotated training tweets. To leverage unlabeled tweets, we build Brown clusters and word vectors, enabling generalizations across distributionally similar words. To leverage annotated newswire data, we employ an importance weighting scheme. Taken all together, we establish a new state-of-the-art on two common test sets. Though it is wellknown that word representations are useful for NER, supporting experiments have thus far focused on newswire data. We emphasize the effectiveness of representations on Twitter NER, and demonstrate that their inclusion can improve performance by up to 20 F1. |
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| Publication date | 2015-05-31 |
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| Publisher | Association for Computational Linguistics |
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| In | |
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| Language | English |
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| Peer reviewed | Yes |
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| NPARC number | 23000026 |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | e5c6c417-b6ac-46e9-bbb5-9b51f3ed233b |
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| Record created | 2016-05-30 |
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| Record modified | 2020-04-22 |
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