Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Normalizador Neural de Datas e Endere\c{c}os   Documents of any kind present a wide variety of date and address formats, in
some cases dates can be written entirely in full or even have different types
of separators. The pattern disorder in addresses is even greater due to the
greater possibility of interchanging between streets, neighborhoods, cities and
states. In the context of natural language processing, problems of this nature
are handled by rigid tools such as ReGex or DateParser, which are efficient as
long as the expected input is pre-configured. When these algorithms are given
an unexpected format, errors and unwanted outputs happen. To circumvent this
challenge, we present a solution with deep neural networks state of art T5 that
treats non-preconfigured formats of dates and addresses with accuracy above 90%
in some cases. With this model, our proposal brings generalization to the task
of normalizing dates and addresses. We also deal with this problem with noisy
data that simulates possible errors in the text.

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The categories of this paper are: cond-mat
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The categories of this paper are: solv-int
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The categories of this paper are: gr-qc
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The categories of this paper are: astro-ph
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The categories of this paper are: chao-dyn
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The categories of this paper are: physics
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The categories of this paper are: patt-sol
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The categories of this paper are: nlin
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The categories of this paper are: adap-org
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The categories of this paper are: econ
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The categories of this paper are: mtrl-th
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The categories of this paper are: cmp-lg
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The categories of this paper are: quant-ph
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The categories of this paper are: atom-ph
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The categories of this paper are: math-ph
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The categories of this paper are: supr-con