Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Expanding Abbreviations in a Strongly Inflected Language: Are
  Morphosyntactic Tags Sufficient?   In this paper, the problem of recovery of morphological information lost in
abbreviated forms is addressed with a focus on highly inflected languages.
Evidence is presented that the correct inflected form of an expanded
abbreviation can in many cases be deduced solely from the morphosyntactic tags
of the context. The prediction model is a deep bidirectional LSTM network with
tag embedding. The training and evaluation data are gathered by finding the
words which could have been abbreviated and using their corresponding
morphosyntactic tags as the labels, while the tags of the context words are
used as the input features for classification. The network is trained on over
10 million words from the Polish Sejm Corpus and achieves 74.2% prediction
accuracy on a smaller, but more general National Corpus of Polish. The analysis
of errors suggests that performance in this task may improve if some prior
knowledge about the abbreviated word is incorporated into the model.

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The categories of this paper are: acc-phys
5
The categories of this paper are: gr-qc
32
The categories of this paper are: atom-ph
7
The categories of this paper are: hep-th
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The categories of this paper are: hep-lat
27
The categories of this paper are: comp-gas
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The categories of this paper are: stat
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The categories of this paper are: alg-geom
0
The categories of this paper are: hep-ph
2
The categories of this paper are: math
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The categories of this paper are: nlin
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The categories of this paper are: eess
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The categories of this paper are: patt-sol
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The categories of this paper are: hep-ex
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The categories of this paper are: astro-ph
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The categories of this paper are: nucl-th