Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: LemMED: Fast and Effective Neural Morphological Analysis with Short
  Context Windows   We present LemMED, a character-level encoder-decoder for contextual
morphological analysis (combined lemmatization and tagging). LemMED extends and
is named after two other attention-based models, namely Lematus, a contextual
lemmatizer, and MED, a morphological (re)inflection model. Our approach does
not require training separate lemmatization and tagging models, nor does it
need additional resources and tools, such as morphological dictionaries or
transducers. Moreover, LemMED relies solely on character-level representations
and on local context. Although the model can, in principle, account for global
context on sentence level, our experiments show that using just a single word
of context around each target word is not only more computationally feasible,
but yields better results as well. We evaluate LemMED in the framework of the
SIMGMORPHON-2019 shared task on combined lemmatization and tagging. In terms of
average performance LemMED ranks 5th among 13 systems and is bested only by the
submissions that use contextualized embeddings.

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The categories of this paper are: bayes-an
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The categories of this paper are: eess
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The categories of this paper are: hep-ex
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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: plasm-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: hep-lat
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The categories of this paper are: stat
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The categories of this paper are: atom-ph
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The categories of this paper are: q-fin
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The categories of this paper are: mtrl-th
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The categories of this paper are: econ
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The categories of this paper are: funct-an
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The categories of this paper are: solv-int
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The categories of this paper are: acc-phys