@inproceedings{pivovarova-etal-2018-benchmarks,
title = "Benchmarks and models for entity-oriented polarity detection",
author = "Pivovarova, Lidia and
Klami, Arto and
Yangarber, Roman",
editor = "Bangalore, Srinivas and
Chu-Carroll, Jennifer and
Li, Yunyao",
booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers)",
month = jun,
year = "2018",
address = "New Orleans - Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N18-3016",
doi = "10.18653/v1/N18-3016",
pages = "129--136",
abstract = "We address the problem of determining entity-oriented polarity in business news. This can be viewed as classifying the polarity of the sentiment expressed toward a given mention of a company in a news article. We present a complete, end-to-end approach to the problem. We introduce a new dataset of over 17,000 manually labeled documents, which is substantially larger than any currently available resources. We propose a benchmark solution based on convolutional neural networks for classifying entity-oriented polarity. Although our dataset is much larger than those currently available, it is small on the scale of datasets commonly used for training robust neural network models. To compensate for this, we use transfer learning{---}pre-train the model on a much larger dataset, annotated for a related but different classification task, in order to learn a good representation for business text, and then fine-tune it on the smaller polarity dataset.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="pivovarova-etal-2018-benchmarks">
<titleInfo>
<title>Benchmarks and models for entity-oriented polarity detection</title>
</titleInfo>
<name type="personal">
<namePart type="given">Lidia</namePart>
<namePart type="family">Pivovarova</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Arto</namePart>
<namePart type="family">Klami</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Roman</namePart>
<namePart type="family">Yangarber</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 3 (Industry Papers)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Srinivas</namePart>
<namePart type="family">Bangalore</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jennifer</namePart>
<namePart type="family">Chu-Carroll</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yunyao</namePart>
<namePart type="family">Li</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>We address the problem of determining entity-oriented polarity in business news. This can be viewed as classifying the polarity of the sentiment expressed toward a given mention of a company in a news article. We present a complete, end-to-end approach to the problem. We introduce a new dataset of over 17,000 manually labeled documents, which is substantially larger than any currently available resources. We propose a benchmark solution based on convolutional neural networks for classifying entity-oriented polarity. Although our dataset is much larger than those currently available, it is small on the scale of datasets commonly used for training robust neural network models. To compensate for this, we use transfer learning—pre-train the model on a much larger dataset, annotated for a related but different classification task, in order to learn a good representation for business text, and then fine-tune it on the smaller polarity dataset.</abstract>
<identifier type="citekey">pivovarova-etal-2018-benchmarks</identifier>
<identifier type="doi">10.18653/v1/N18-3016</identifier>
<location>
<url>https://aclanthology.org/N18-3016</url>
</location>
<part>
<date>2018-06</date>
<extent unit="page">
<start>129</start>
<end>136</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Benchmarks and models for entity-oriented polarity detection
%A Pivovarova, Lidia
%A Klami, Arto
%A Yangarber, Roman
%Y Bangalore, Srinivas
%Y Chu-Carroll, Jennifer
%Y Li, Yunyao
%S Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers)
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans - Louisiana
%F pivovarova-etal-2018-benchmarks
%X We address the problem of determining entity-oriented polarity in business news. This can be viewed as classifying the polarity of the sentiment expressed toward a given mention of a company in a news article. We present a complete, end-to-end approach to the problem. We introduce a new dataset of over 17,000 manually labeled documents, which is substantially larger than any currently available resources. We propose a benchmark solution based on convolutional neural networks for classifying entity-oriented polarity. Although our dataset is much larger than those currently available, it is small on the scale of datasets commonly used for training robust neural network models. To compensate for this, we use transfer learning—pre-train the model on a much larger dataset, annotated for a related but different classification task, in order to learn a good representation for business text, and then fine-tune it on the smaller polarity dataset.
%R 10.18653/v1/N18-3016
%U https://aclanthology.org/N18-3016
%U https://doi.org/10.18653/v1/N18-3016
%P 129-136
Markdown (Informal)
[Benchmarks and models for entity-oriented polarity detection](https://aclanthology.org/N18-3016) (Pivovarova et al., NAACL 2018)
ACL
- Lidia Pivovarova, Arto Klami, and Roman Yangarber. 2018. Benchmarks and models for entity-oriented polarity detection. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers), pages 129–136, New Orleans - Louisiana. Association for Computational Linguistics.