@inproceedings{eshel-etal-2017-named,
title = "Named Entity Disambiguation for Noisy Text",
author = "Eshel, Yotam and
Cohen, Noam and
Radinsky, Kira and
Markovitch, Shaul and
Yamada, Ikuya and
Levy, Omer",
editor = "Levy, Roger and
Specia, Lucia",
booktitle = "Proceedings of the 21st Conference on Computational Natural Language Learning ({C}o{NLL} 2017)",
month = aug,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/K17-1008",
doi = "10.18653/v1/K17-1008",
pages = "58--68",
abstract = "We address the task of Named Entity Disambiguation (NED) for noisy text. We present WikilinksNED, a large-scale NED dataset of text fragments from the web, which is significantly noisier and more challenging than existing news-based datasets. To capture the limited and noisy local context surrounding each mention, we design a neural model and train it with a novel method for sampling informative negative examples. We also describe a new way of initializing word and entity embeddings that significantly improves performance. Our model significantly outperforms existing state-of-the-art methods on WikilinksNED while achieving comparable performance on a smaller newswire dataset.",
}
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<abstract>We address the task of Named Entity Disambiguation (NED) for noisy text. We present WikilinksNED, a large-scale NED dataset of text fragments from the web, which is significantly noisier and more challenging than existing news-based datasets. To capture the limited and noisy local context surrounding each mention, we design a neural model and train it with a novel method for sampling informative negative examples. We also describe a new way of initializing word and entity embeddings that significantly improves performance. Our model significantly outperforms existing state-of-the-art methods on WikilinksNED while achieving comparable performance on a smaller newswire dataset.</abstract>
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%0 Conference Proceedings
%T Named Entity Disambiguation for Noisy Text
%A Eshel, Yotam
%A Cohen, Noam
%A Radinsky, Kira
%A Markovitch, Shaul
%A Yamada, Ikuya
%A Levy, Omer
%Y Levy, Roger
%Y Specia, Lucia
%S Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada
%F eshel-etal-2017-named
%X We address the task of Named Entity Disambiguation (NED) for noisy text. We present WikilinksNED, a large-scale NED dataset of text fragments from the web, which is significantly noisier and more challenging than existing news-based datasets. To capture the limited and noisy local context surrounding each mention, we design a neural model and train it with a novel method for sampling informative negative examples. We also describe a new way of initializing word and entity embeddings that significantly improves performance. Our model significantly outperforms existing state-of-the-art methods on WikilinksNED while achieving comparable performance on a smaller newswire dataset.
%R 10.18653/v1/K17-1008
%U https://aclanthology.org/K17-1008
%U https://doi.org/10.18653/v1/K17-1008
%P 58-68
Markdown (Informal)
[Named Entity Disambiguation for Noisy Text](https://aclanthology.org/K17-1008) (Eshel et al., CoNLL 2017)
ACL
- Yotam Eshel, Noam Cohen, Kira Radinsky, Shaul Markovitch, Ikuya Yamada, and Omer Levy. 2017. Named Entity Disambiguation for Noisy Text. In Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017), pages 58–68, Vancouver, Canada. Association for Computational Linguistics.