Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Neural Semi-Markov Conditional Random Fields for Robust Character-Based
  Part-of-Speech Tagging   Character-level models of tokens have been shown to be effective at dealing
with within-token noise and out-of-vocabulary words. But these models still
rely on correct token boundaries. In this paper, we propose a novel end-to-end
character-level model and demonstrate its effectiveness in multilingual
settings and when token boundaries are noisy. Our model is a semi-Markov
conditional random field with neural networks for character and segment
representation. It requires no tokenizer. The model matches state-of-the-art
baselines for various languages and significantly outperforms them on a noisy
English version of a part-of-speech tagging benchmark dataset. Our code and the
noisy dataset are publicly available at http://cistern.cis.lmu.de/semiCRF.

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The categories of this paper are: supr-con
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The categories of this paper are: chem-ph
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The categories of this paper are: funct-an
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The categories of this paper are: gr-qc
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The categories of this paper are: nucl-ex
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The categories of this paper are: hep-ph
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The categories of this paper are: ao-sci
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The categories of this paper are: quant-ph
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The categories of this paper are: q-alg
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The categories of this paper are: solv-int
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The categories of this paper are: chao-dyn
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The categories of this paper are: cmp-lg
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The categories of this paper are: q-fin
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The categories of this paper are: eess
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The categories of this paper are: hep-th
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The categories of this paper are: adap-org