Attention-based LSTM-CNNs for uncertainty identification on Chinese social media texts

Uncertainty identification is an important semantic processing task, which is crucial to the quality of information in terms of factuality in many techniques, e.g. topic detection, question answering. Especially in social media, the texts are written informally which are widely used in many applicat...

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Main Authors: LI, Binyang, ZHOU, Kaiming, GAO, Wei, HAN, Xu Han, ZHOU, Linna
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Language:English
Published: Institutional Knowledge at Singapore Management University 2018
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Online Access:https://ink.library.smu.edu.sg/sis_research/4566
https://ink.library.smu.edu.sg/context/sis_research/article/5569/viewcontent/109_ICSPAC_2017_paper_130.pdf
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spelling sg-smu-ink.sis_research-55692019-12-26T08:24:52Z Attention-based LSTM-CNNs for uncertainty identification on Chinese social media texts LI, Binyang ZHOU, Kaiming GAO, Wei HAN, Xu Han ZHOU, Linna Uncertainty identification is an important semantic processing task, which is crucial to the quality of information in terms of factuality in many techniques, e.g. topic detection, question answering. Especially in social media, the texts are written informally which are widely used in many applications, so the factuality has become a premier concern. However, existing approaches that still rely on lexical cues suffer greatly from the casual or word-of-mouth peculiarity of social media, in which the cue phrases are often expressed in sub-standard form or even omitted from sentences. To tackle these problems, this paper proposes the attention-based LSTM-CNNs for the uncertainty identification on social media texts, named ALUNI. ALUNI incorporates attention-based LSTM networks to represent the semantics of words, and convolutional neural networks to capture the most important semantics of uncertainty for identification. Experiments are conducted on both Chinese Weibo and news datasets, and 78.19% and 73.95% of F1-measure scores are achieved with 11% and 3% improvement over the baseline, respectively. 2018-12-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/4566 info:doi/10.1109/SPAC.2017.8304349 https://ink.library.smu.edu.sg/context/sis_research/article/5569/viewcontent/109_ICSPAC_2017_paper_130.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Databases and Information Systems
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Databases and Information Systems
spellingShingle Databases and Information Systems
LI, Binyang
ZHOU, Kaiming
GAO, Wei
HAN, Xu Han
ZHOU, Linna
Attention-based LSTM-CNNs for uncertainty identification on Chinese social media texts
description Uncertainty identification is an important semantic processing task, which is crucial to the quality of information in terms of factuality in many techniques, e.g. topic detection, question answering. Especially in social media, the texts are written informally which are widely used in many applications, so the factuality has become a premier concern. However, existing approaches that still rely on lexical cues suffer greatly from the casual or word-of-mouth peculiarity of social media, in which the cue phrases are often expressed in sub-standard form or even omitted from sentences. To tackle these problems, this paper proposes the attention-based LSTM-CNNs for the uncertainty identification on social media texts, named ALUNI. ALUNI incorporates attention-based LSTM networks to represent the semantics of words, and convolutional neural networks to capture the most important semantics of uncertainty for identification. Experiments are conducted on both Chinese Weibo and news datasets, and 78.19% and 73.95% of F1-measure scores are achieved with 11% and 3% improvement over the baseline, respectively.
format text
author LI, Binyang
ZHOU, Kaiming
GAO, Wei
HAN, Xu Han
ZHOU, Linna
author_facet LI, Binyang
ZHOU, Kaiming
GAO, Wei
HAN, Xu Han
ZHOU, Linna
author_sort LI, Binyang
title Attention-based LSTM-CNNs for uncertainty identification on Chinese social media texts
title_short Attention-based LSTM-CNNs for uncertainty identification on Chinese social media texts
title_full Attention-based LSTM-CNNs for uncertainty identification on Chinese social media texts
title_fullStr Attention-based LSTM-CNNs for uncertainty identification on Chinese social media texts
title_full_unstemmed Attention-based LSTM-CNNs for uncertainty identification on Chinese social media texts
title_sort attention-based lstm-cnns for uncertainty identification on chinese social media texts
publisher Institutional Knowledge at Singapore Management University
publishDate 2018
url https://ink.library.smu.edu.sg/sis_research/4566
https://ink.library.smu.edu.sg/context/sis_research/article/5569/viewcontent/109_ICSPAC_2017_paper_130.pdf
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