Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Distant Supervision and Noisy Label Learning for Low Resource Named
  Entity Recognition: A Study on Hausa and Yor\`ub\'a   The lack of labeled training data has limited the development of natural
language processing tools, such as named entity recognition, for many languages
spoken in developing countries. Techniques such as distant and weak supervision
can be used to create labeled data in a (semi-) automatic way. Additionally, to
alleviate some of the negative effects of the errors in automatic annotation,
noise-handling methods can be integrated. Pretrained word embeddings are
another key component of most neural named entity classifiers. With the advent
of more complex contextual word embeddings, an interesting trade-off between
model size and performance arises. While these techniques have been shown to
work well in high-resource settings, we want to study how they perform in
low-resource scenarios. In this work, we perform named entity recognition for
Hausa and Yor\`ub\'a, two languages that are widely spoken in several
developing countries. We evaluate different embedding approaches and show that
distant supervision can be successfully leveraged in a realistic low-resource
scenario where it can more than double a classifier's performance.

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The categories of this paper are: hep-th
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The categories of this paper are: comp-gas
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The categories of this paper are: nlin
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The categories of this paper are: nucl-ex
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The categories of this paper are: quant-ph
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The categories of this paper are: q-fin
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The categories of this paper are: math
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The categories of this paper are: math-ph
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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-alg
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The categories of this paper are: cmp-lg
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The categories of this paper are: dg-ga
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The categories of this paper are: bayes-an
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The categories of this paper are: chem-ph
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The categories of this paper are: hep-lat