Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: EELECTION at SemEval-2017 Task 10: Ensemble of nEural Learners for
  kEyphrase ClassificaTION   This paper describes our approach to the SemEval 2017 Task 10: "Extracting
Keyphrases and Relations from Scientific Publications", specifically to Subtask
(B): "Classification of identified keyphrases". We explored three different
deep learning approaches: a character-level convolutional neural network (CNN),
a stacked learner with an MLP meta-classifier, and an attention based Bi-LSTM.
From these approaches, we created an ensemble of differently
hyper-parameterized systems, achieving a micro-F1-score of 0.63 on the test
data. Our approach ranks 2nd (score of 1st placed system: 0.64) out of four
according to this official score. However, we erroneously trained 2 out of 3
neural nets (the stacker and the CNN) on only roughly 15% of the full data,
namely, the original development set. When trained on the full data
(training+development), our ensemble has a micro-F1-score of 0.69. Our code is
available from https://github.com/UKPLab/semeval2017-scienceie.

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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: dg-ga
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The categories of this paper are: hep-ex
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The categories of this paper are: acc-phys
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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: eess
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The categories of this paper are: quant-ph
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The categories of this paper are: adap-org
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The categories of this paper are: plasm-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: q-bio
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The categories of this paper are: astro-ph
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The categories of this paper are: hep-lat
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The categories of this paper are: funct-an