@inproceedings{mansar-ferradans-2018-sentence,
title = "Sentence Classification for Investment Rules Detection",
author = "Mansar, Youness and
Ferradans, Sira",
editor = "Hahn, Udo and
Hoste, V{\'e}ronique and
Tsai, Ming-Feng",
booktitle = "Proceedings of the First Workshop on Economics and Natural Language Processing",
month = jul,
year = "2018",
address = "Melbourne, Australia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-3106",
doi = "10.18653/v1/W18-3106",
pages = "44--48",
abstract = "In the last years, compliance requirements for the banking sector have greatly augmented, making the current compliance processes difficult to maintain. Any process that allows to accelerate the identification and implementation of compliance requirements can help address this issues. The contributions of the paper are twofold: we propose a new NLP task that is the investment rule detection, and a group of methods identify them. We show that the proposed methods are highly performing and fast, thus can be deployed in production.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="mansar-ferradans-2018-sentence">
<titleInfo>
<title>Sentence Classification for Investment Rules Detection</title>
</titleInfo>
<name type="personal">
<namePart type="given">Youness</namePart>
<namePart type="family">Mansar</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sira</namePart>
<namePart type="family">Ferradans</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2018-07</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the First Workshop on Economics and Natural Language Processing</title>
</titleInfo>
<name type="personal">
<namePart type="given">Udo</namePart>
<namePart type="family">Hahn</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Véronique</namePart>
<namePart type="family">Hoste</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ming-Feng</namePart>
<namePart type="family">Tsai</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Melbourne, Australia</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>In the last years, compliance requirements for the banking sector have greatly augmented, making the current compliance processes difficult to maintain. Any process that allows to accelerate the identification and implementation of compliance requirements can help address this issues. The contributions of the paper are twofold: we propose a new NLP task that is the investment rule detection, and a group of methods identify them. We show that the proposed methods are highly performing and fast, thus can be deployed in production.</abstract>
<identifier type="citekey">mansar-ferradans-2018-sentence</identifier>
<identifier type="doi">10.18653/v1/W18-3106</identifier>
<location>
<url>https://aclanthology.org/W18-3106</url>
</location>
<part>
<date>2018-07</date>
<extent unit="page">
<start>44</start>
<end>48</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Sentence Classification for Investment Rules Detection
%A Mansar, Youness
%A Ferradans, Sira
%Y Hahn, Udo
%Y Hoste, Véronique
%Y Tsai, Ming-Feng
%S Proceedings of the First Workshop on Economics and Natural Language Processing
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F mansar-ferradans-2018-sentence
%X In the last years, compliance requirements for the banking sector have greatly augmented, making the current compliance processes difficult to maintain. Any process that allows to accelerate the identification and implementation of compliance requirements can help address this issues. The contributions of the paper are twofold: we propose a new NLP task that is the investment rule detection, and a group of methods identify them. We show that the proposed methods are highly performing and fast, thus can be deployed in production.
%R 10.18653/v1/W18-3106
%U https://aclanthology.org/W18-3106
%U https://doi.org/10.18653/v1/W18-3106
%P 44-48
Markdown (Informal)
[Sentence Classification for Investment Rules Detection](https://aclanthology.org/W18-3106) (Mansar & Ferradans, ACL 2018)
ACL