Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Named Entity Disambiguation for Noisy Text   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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The categories of this paper are: mtrl-th
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The categories of this paper are: bayes-an
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The categories of this paper are: nlin
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The categories of this paper are: gr-qc
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The categories of this paper are: dg-ga
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The categories of this paper are: econ
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The categories of this paper are: comp-gas
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The categories of this paper are: plasm-ph
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The categories of this paper are: math-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: nucl-ex
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The categories of this paper are: nucl-th
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The categories of this paper are: atom-ph
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The categories of this paper are: cmp-lg
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The categories of this paper are: hep-ex
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The categories of this paper are: ao-sci