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arXiv · 1706.09147

Named Entity Disambiguation for Noisy Text

Yotam Eshel·Noam Cohen·Kira Radinsky·Shaul Markovitch·Ikuya Yamada·Omer Levy
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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.

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Yotam Eshel, Noam Cohen, Kira Radinsky, Shaul Markovitch, Ikuya Yamada, Omer Levy. 2017-06-28. Named Entity Disambiguation for Noisy Text. https://arxiv.org/abs/1706.09147

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