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    <titleInfo>
        <title>Automatic Extraction of Entities and Relation from Legal Documents</title>
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            <title>Proceedings of the Seventh Named Entities Workshop</title>
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            <namePart type="given">Nancy</namePart>
            <namePart type="family">Chen</namePart>
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        <name type="personal">
            <namePart type="given">Rafael</namePart>
            <namePart type="given">E</namePart>
            <namePart type="family">Banchs</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Xiangyu</namePart>
            <namePart type="family">Duan</namePart>
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    <abstract>In recent years, the journalists and computer sciences speak to each other to identify useful technologies which would help them in extracting useful information. This is called “computational Journalism”. In this paper, we present a method that will enable the journalists to automatically identifies and annotates entities such as names of people, organizations, role and functions of people in legal documents; the relationship between these entities are also explored. The system uses a combination of both statistical and rule based technique. The statistical method used is Conditional Random Fields and for the rule based technique, document and language specific regular expressions are used.</abstract>
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%0 Conference Proceedings
%T Automatic Extraction of Entities and Relation from Legal Documents
%A Andrew, Judith Jeyafreeda
%Y Chen, Nancy
%Y Banchs, Rafael E.
%Y Duan, Xiangyu
%Y Zhang, Min
%Y Li, Haizhou
%S Proceedings of the Seventh Named Entities Workshop
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F andrew-2018-automatic
%X In recent years, the journalists and computer sciences speak to each other to identify useful technologies which would help them in extracting useful information. This is called “computational Journalism”. In this paper, we present a method that will enable the journalists to automatically identifies and annotates entities such as names of people, organizations, role and functions of people in legal documents; the relationship between these entities are also explored. The system uses a combination of both statistical and rule based technique. The statistical method used is Conditional Random Fields and for the rule based technique, document and language specific regular expressions are used.
%R 10.18653/v1/W18-2401
%U https://aclanthology.org/W18-2401
%U https://doi.org/10.18653/v1/W18-2401
%P 1-8

Markdown (Informal)

[Automatic Extraction of Entities and Relation from Legal Documents](https://aclanthology.org/W18-2401) (Andrew, NEWS 2018)

• Automatic Extraction of Entities and Relation from Legal Documents (Andrew, NEWS 2018)

ACL

• Judith Jeyafreeda Andrew. 2018. Automatic Extraction of Entities and Relation from Legal Documents. In Proceedings of the Seventh Named Entities Workshop, pages 1–8, Melbourne, Australia. Association for Computational Linguistics.