@inproceedings{lopez-monroy-etal-2018-early,
title = "Early Text Classification Using Multi-Resolution Concept Representations",
author = "L{\'o}pez-Monroy, Adrian Pastor and
Gonz{\'a}lez, Fabio A. and
Montes, Manuel and
Escalante, Hugo Jair and
Solorio, Thamar",
editor = "Walker, Marilyn and
Ji, Heng and
Stent, Amanda",
booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N18-1110",
doi = "10.18653/v1/N18-1110",
pages = "1216--1225",
abstract = "The intensive use of e-communications in everyday life has given rise to new threats and risks. When the vulnerable asset is the user, detecting these potential attacks before they cause serious damages is extremely important. This paper proposes a novel document representation to improve the early detection of risks in social media sources. The goal is to effectively identify the potential risk using as few text as possible and with as much anticipation as possible. Accordingly, we devise a Multi-Resolution Representation (MulR), which allows us to generate multiple {``}views{''} of the analyzed text. These views capture different semantic meanings for words and documents at different levels of detail, which is very useful in early scenarios to model the variable amounts of evidence. Intuitively, the representation captures better the content of short documents (very early stages) in low resolutions, whereas large documents (medium/large stages) are better modeled with higher resolutions. We evaluate the proposed ideas in two different tasks where anticipation is critical: sexual predator detection and depression detection. The experimental evaluation for these early tasks revealed that the proposed approach outperforms previous methodologies by a considerable margin.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="lopez-monroy-etal-2018-early">
<titleInfo>
<title>Early Text Classification Using Multi-Resolution Concept Representations</title>
</titleInfo>
<name type="personal">
<namePart type="given">Adrian</namePart>
<namePart type="given">Pastor</namePart>
<namePart type="family">López-Monroy</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Fabio</namePart>
<namePart type="given">A</namePart>
<namePart type="family">González</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Manuel</namePart>
<namePart type="family">Montes</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Hugo</namePart>
<namePart type="given">Jair</namePart>
<namePart type="family">Escalante</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Thamar</namePart>
<namePart type="family">Solorio</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2018-06</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Marilyn</namePart>
<namePart type="family">Walker</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Heng</namePart>
<namePart type="family">Ji</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Amanda</namePart>
<namePart type="family">Stent</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">New Orleans, Louisiana</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>The intensive use of e-communications in everyday life has given rise to new threats and risks. When the vulnerable asset is the user, detecting these potential attacks before they cause serious damages is extremely important. This paper proposes a novel document representation to improve the early detection of risks in social media sources. The goal is to effectively identify the potential risk using as few text as possible and with as much anticipation as possible. Accordingly, we devise a Multi-Resolution Representation (MulR), which allows us to generate multiple “views” of the analyzed text. These views capture different semantic meanings for words and documents at different levels of detail, which is very useful in early scenarios to model the variable amounts of evidence. Intuitively, the representation captures better the content of short documents (very early stages) in low resolutions, whereas large documents (medium/large stages) are better modeled with higher resolutions. We evaluate the proposed ideas in two different tasks where anticipation is critical: sexual predator detection and depression detection. The experimental evaluation for these early tasks revealed that the proposed approach outperforms previous methodologies by a considerable margin.</abstract>
<identifier type="citekey">lopez-monroy-etal-2018-early</identifier>
<identifier type="doi">10.18653/v1/N18-1110</identifier>
<location>
<url>https://aclanthology.org/N18-1110</url>
</location>
<part>
<date>2018-06</date>
<extent unit="page">
<start>1216</start>
<end>1225</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Early Text Classification Using Multi-Resolution Concept Representations
%A López-Monroy, Adrian Pastor
%A González, Fabio A.
%A Montes, Manuel
%A Escalante, Hugo Jair
%A Solorio, Thamar
%Y Walker, Marilyn
%Y Ji, Heng
%Y Stent, Amanda
%S Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F lopez-monroy-etal-2018-early
%X The intensive use of e-communications in everyday life has given rise to new threats and risks. When the vulnerable asset is the user, detecting these potential attacks before they cause serious damages is extremely important. This paper proposes a novel document representation to improve the early detection of risks in social media sources. The goal is to effectively identify the potential risk using as few text as possible and with as much anticipation as possible. Accordingly, we devise a Multi-Resolution Representation (MulR), which allows us to generate multiple “views” of the analyzed text. These views capture different semantic meanings for words and documents at different levels of detail, which is very useful in early scenarios to model the variable amounts of evidence. Intuitively, the representation captures better the content of short documents (very early stages) in low resolutions, whereas large documents (medium/large stages) are better modeled with higher resolutions. We evaluate the proposed ideas in two different tasks where anticipation is critical: sexual predator detection and depression detection. The experimental evaluation for these early tasks revealed that the proposed approach outperforms previous methodologies by a considerable margin.
%R 10.18653/v1/N18-1110
%U https://aclanthology.org/N18-1110
%U https://doi.org/10.18653/v1/N18-1110
%P 1216-1225
Markdown (Informal)
[Early Text Classification Using Multi-Resolution Concept Representations](https://aclanthology.org/N18-1110) (López-Monroy et al., NAACL 2018)
ACL
- Adrian Pastor López-Monroy, Fabio A. González, Manuel Montes, Hugo Jair Escalante, and Thamar Solorio. 2018. Early Text Classification Using Multi-Resolution Concept Representations. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 1216–1225, New Orleans, Louisiana. Association for Computational Linguistics.