@inproceedings{jacobs-etal-2018-economic,
title = "Economic Event Detection in Company-Specific News Text",
author = "Jacobs, Gilles and
Lefever, Els and
Hoste, V{\'e}ronique",
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/W18-3101",
doi = "10.18653/v1/W18-3101",
pages = "1--10",
abstract = "This paper presents a dataset and supervised classification approach for economic event detection in English news articles. Currently, the economic domain is lacking resources and methods for data-driven supervised event detection. The detection task is conceived as a sentence-level classification task for 10 different economic event types. Two different machine learning approaches were tested: a rich feature set Support Vector Machine (SVM) set-up and a word-vector-based long short-term memory recurrent neural network (RNN-LSTM) set-up. We show satisfactory results for most event types, with the linear kernel SVM outperforming the other experimental set-ups",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="jacobs-etal-2018-economic">
<titleInfo>
<title>Economic Event Detection in Company-Specific News Text</title>
</titleInfo>
<name type="personal">
<namePart type="given">Gilles</namePart>
<namePart type="family">Jacobs</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Els</namePart>
<namePart type="family">Lefever</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Véronique</namePart>
<namePart type="family">Hoste</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>This paper presents a dataset and supervised classification approach for economic event detection in English news articles. Currently, the economic domain is lacking resources and methods for data-driven supervised event detection. The detection task is conceived as a sentence-level classification task for 10 different economic event types. Two different machine learning approaches were tested: a rich feature set Support Vector Machine (SVM) set-up and a word-vector-based long short-term memory recurrent neural network (RNN-LSTM) set-up. We show satisfactory results for most event types, with the linear kernel SVM outperforming the other experimental set-ups</abstract>
<identifier type="citekey">jacobs-etal-2018-economic</identifier>
<identifier type="doi">10.18653/v1/W18-3101</identifier>
<location>
<url>https://aclanthology.org/W18-3101</url>
</location>
<part>
<date>2018-07</date>
<extent unit="page">
<start>1</start>
<end>10</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Economic Event Detection in Company-Specific News Text
%A Jacobs, Gilles
%A Lefever, Els
%A Hoste, Véronique
%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 jacobs-etal-2018-economic
%X This paper presents a dataset and supervised classification approach for economic event detection in English news articles. Currently, the economic domain is lacking resources and methods for data-driven supervised event detection. The detection task is conceived as a sentence-level classification task for 10 different economic event types. Two different machine learning approaches were tested: a rich feature set Support Vector Machine (SVM) set-up and a word-vector-based long short-term memory recurrent neural network (RNN-LSTM) set-up. We show satisfactory results for most event types, with the linear kernel SVM outperforming the other experimental set-ups
%R 10.18653/v1/W18-3101
%U https://aclanthology.org/W18-3101
%U https://doi.org/10.18653/v1/W18-3101
%P 1-10
Markdown (Informal)
[Economic Event Detection in Company-Specific News Text](https://aclanthology.org/W18-3101) (Jacobs et al., ACL 2018)
ACL
- Gilles Jacobs, Els Lefever, and Véronique Hoste. 2018. Economic Event Detection in Company-Specific News Text. In Proceedings of the First Workshop on Economics and Natural Language Processing, pages 1–10, Melbourne, Australia. Association for Computational Linguistics.