@article{yang-cardie-2014-joint,
title = "Joint Modeling of Opinion Expression Extraction and Attribute Classification",
author = "Yang, Bishan and
Cardie, Claire",
editor = "Lin, Dekang and
Collins, Michael and
Lee, Lillian",
journal = "Transactions of the Association for Computational Linguistics",
volume = "2",
year = "2014",
address = "Cambridge, MA",
publisher = "MIT Press",
url = "https://aclanthology.org/Q14-1039/",
doi = "10.1162/tacl_a_00199",
pages = "505--516",
abstract = "In this paper, we study the problems of opinion expression extraction and expression-level polarity and intensity classification. Traditional fine-grained opinion analysis systems address these problems in isolation and thus cannot capture interactions among the textual spans of opinion expressions and their opinion-related properties. We present two types of joint approaches that can account for such interactions during 1) both learning and inference or 2) only during inference. Extensive experiments on a standard dataset demonstrate that our approaches provide substantial improvements over previously published results. By analyzing the results, we gain some insight into the advantages of different joint models."
}
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%0 Journal Article
%T Joint Modeling of Opinion Expression Extraction and Attribute Classification
%A Yang, Bishan
%A Cardie, Claire
%J Transactions of the Association for Computational Linguistics
%D 2014
%V 2
%I MIT Press
%C Cambridge, MA
%F yang-cardie-2014-joint
%X In this paper, we study the problems of opinion expression extraction and expression-level polarity and intensity classification. Traditional fine-grained opinion analysis systems address these problems in isolation and thus cannot capture interactions among the textual spans of opinion expressions and their opinion-related properties. We present two types of joint approaches that can account for such interactions during 1) both learning and inference or 2) only during inference. Extensive experiments on a standard dataset demonstrate that our approaches provide substantial improvements over previously published results. By analyzing the results, we gain some insight into the advantages of different joint models.
%R 10.1162/tacl_a_00199
%U https://aclanthology.org/Q14-1039/
%U https://doi.org/10.1162/tacl_a_00199
%P 505-516
Markdown (Informal)
[Joint Modeling of Opinion Expression Extraction and Attribute Classification](https://aclanthology.org/Q14-1039/) (Yang & Cardie, TACL 2014)
ACL