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    <titleInfo>
        <title>Related Named Entities Classification in the Economic-Financial Context</title>
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    <name type="personal">
        <namePart type="given">Daniel</namePart>
        <namePart type="family">De Los Reyes</namePart>
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    <name type="personal">
        <namePart type="given">Allan</namePart>
        <namePart type="family">Barcelos</namePart>
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    <name type="personal">
        <namePart type="given">Renata</namePart>
        <namePart type="family">Vieira</namePart>
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            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
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    <name type="personal">
        <namePart type="given">Isabel</namePart>
        <namePart type="family">Manssour</namePart>
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            <title>Proceedings of the EACL Hackashop on News Media Content Analysis and Automated Report Generation</title>
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            <namePart type="given">Hannu</namePart>
            <namePart type="family">Toivonen</namePart>
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        <name type="personal">
            <namePart type="given">Michele</namePart>
            <namePart type="family">Boggia</namePart>
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            <publisher>Association for Computational Linguistics</publisher>
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    <abstract>The present work uses the Bidirectional Encoder Representations from Transformers (BERT) to process a sentence and its entities and indicate whether two named entities present in a sentence are related or not, constituting a binary classification problem. It was developed for the Portuguese language, considering the financial domain and exploring deep linguistic representations to identify a relation between entities without using other lexical-semantic resources. The results of the experiments show an accuracy of 86% of the predictions.</abstract>
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        <url>https://aclanthology.org/2021.hackashop-1.2</url>
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        <date>2021-04</date>
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            <start>8</start>
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%0 Conference Proceedings
%T Related Named Entities Classification in the Economic-Financial Context
%A De Los Reyes, Daniel
%A Barcelos, Allan
%A Vieira, Renata
%A Manssour, Isabel
%Y Toivonen, Hannu
%Y Boggia, Michele
%S Proceedings of the EACL Hackashop on News Media Content Analysis and Automated Report Generation
%D 2021
%8 April
%I Association for Computational Linguistics
%C Online
%F de-los-reyes-etal-2021-related
%X The present work uses the Bidirectional Encoder Representations from Transformers (BERT) to process a sentence and its entities and indicate whether two named entities present in a sentence are related or not, constituting a binary classification problem. It was developed for the Portuguese language, considering the financial domain and exploring deep linguistic representations to identify a relation between entities without using other lexical-semantic resources. The results of the experiments show an accuracy of 86% of the predictions.
%U https://aclanthology.org/2021.hackashop-1.2
%P 8-15

Markdown (Informal)

[Related Named Entities Classification in the Economic-Financial Context](https://aclanthology.org/2021.hackashop-1.2) (De Los Reyes et al., Hackashop 2021)

• Related Named Entities Classification in the Economic-Financial Context (De Los Reyes et al., Hackashop 2021)

ACL

• Daniel De Los Reyes, Allan Barcelos, Renata Vieira, and Isabel Manssour. 2021. Related Named Entities Classification in the Economic-Financial Context. In Proceedings of the EACL Hackashop on News Media Content Analysis and Automated Report Generation, pages 8–15, Online. Association for Computational Linguistics.