Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Robust Lexical Features for Improved Neural Network Named-Entity
  Recognition   Neural network approaches to Named-Entity Recognition reduce the need for
carefully hand-crafted features. While some features do remain in
state-of-the-art systems, lexical features have been mostly discarded, with the
exception of gazetteers. In this work, we show that this is unfair: lexical
features are actually quite useful. We propose to embed words and entity types
into a low-dimensional vector space we train from annotated data produced by
distant supervision thanks to Wikipedia. From this, we compute - offline - a
feature vector representing each word. When used with a vanilla recurrent
neural network model, this representation yields substantial improvements. We
establish a new state-of-the-art F1 score of 87.95 on ONTONOTES 5.0, while
matching state-of-the-art performance with a F1 score of 91.73 on the
over-studied CONLL-2003 dataset.

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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: supr-con
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The categories of this paper are: physics
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The categories of this paper are: bayes-an
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The categories of this paper are: comp-gas
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The categories of this paper are: q-fin
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The categories of this paper are: ao-sci
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The categories of this paper are: dg-ga
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The categories of this paper are: solv-int
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The categories of this paper are: funct-an
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The categories of this paper are: hep-lat
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The categories of this paper are: math-ph
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The categories of this paper are: cond-mat
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The categories of this paper are: nucl-ex
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The categories of this paper are: astro-ph