@inproceedings{zhang-etal-2022-ecnu,
title = "{ECNU}{\_}{ICA} at {S}em{E}val-2022 Task 10: A Simple and Unified Model for Monolingual and Crosslingual Structured Sentiment Analysis",
author = "Zhang, Qi and
Zhou, Jie and
Chen, Qin and
Bai, Qingchun and
Xiao, Jun and
He, Liang",
editor = "Emerson, Guy and
Schluter, Natalie and
Stanovsky, Gabriel and
Kumar, Ritesh and
Palmer, Alexis and
Schneider, Nathan and
Singh, Siddharth and
Ratan, Shyam",
booktitle = "Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.semeval-1.186/",
doi = "10.18653/v1/2022.semeval-1.186",
pages = "1336--1342",
abstract = "Sentiment analysis is increasingly viewed as a vital task both from an academic and a commercial standpoint. In this paper, we focus on the structured sentiment analysis task that is released on SemEval-2022 Task 10. The task aims to extract the structured sentiment information (e.g., holder, target, expression and sentiment polarity) in a text. We propose a simple and unified model for both the monolingual and crosslingual structured sentiment analysis tasks. We translate this task into an event extraction task by regrading the expression as the trigger word and the other elements as the arguments of the event. Particularly, we first extract the expression by judging its start and end indices. Then, to consider the expression, we design a conditional layer normalization algorithm to extract the holder and target based on the extracted expression. Finally, we infer the sentiment polarity based on the extracted structured information. Pre-trained language models are utilized to obtain the text representation. We conduct the experiments on seven datasets in five languages. It attracted 233 submissions in monolingual subtask and crosslingual subtask from 32 teams. Finally, we obtain the top 5 place on crosslingual tasks."
}
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<abstract>Sentiment analysis is increasingly viewed as a vital task both from an academic and a commercial standpoint. In this paper, we focus on the structured sentiment analysis task that is released on SemEval-2022 Task 10. The task aims to extract the structured sentiment information (e.g., holder, target, expression and sentiment polarity) in a text. We propose a simple and unified model for both the monolingual and crosslingual structured sentiment analysis tasks. We translate this task into an event extraction task by regrading the expression as the trigger word and the other elements as the arguments of the event. Particularly, we first extract the expression by judging its start and end indices. Then, to consider the expression, we design a conditional layer normalization algorithm to extract the holder and target based on the extracted expression. Finally, we infer the sentiment polarity based on the extracted structured information. Pre-trained language models are utilized to obtain the text representation. We conduct the experiments on seven datasets in five languages. It attracted 233 submissions in monolingual subtask and crosslingual subtask from 32 teams. Finally, we obtain the top 5 place on crosslingual tasks.</abstract>
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%0 Conference Proceedings
%T ECNU_ICA at SemEval-2022 Task 10: A Simple and Unified Model for Monolingual and Crosslingual Structured Sentiment Analysis
%A Zhang, Qi
%A Zhou, Jie
%A Chen, Qin
%A Bai, Qingchun
%A Xiao, Jun
%A He, Liang
%Y Emerson, Guy
%Y Schluter, Natalie
%Y Stanovsky, Gabriel
%Y Kumar, Ritesh
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y Singh, Siddharth
%Y Ratan, Shyam
%S Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F zhang-etal-2022-ecnu
%X Sentiment analysis is increasingly viewed as a vital task both from an academic and a commercial standpoint. In this paper, we focus on the structured sentiment analysis task that is released on SemEval-2022 Task 10. The task aims to extract the structured sentiment information (e.g., holder, target, expression and sentiment polarity) in a text. We propose a simple and unified model for both the monolingual and crosslingual structured sentiment analysis tasks. We translate this task into an event extraction task by regrading the expression as the trigger word and the other elements as the arguments of the event. Particularly, we first extract the expression by judging its start and end indices. Then, to consider the expression, we design a conditional layer normalization algorithm to extract the holder and target based on the extracted expression. Finally, we infer the sentiment polarity based on the extracted structured information. Pre-trained language models are utilized to obtain the text representation. We conduct the experiments on seven datasets in five languages. It attracted 233 submissions in monolingual subtask and crosslingual subtask from 32 teams. Finally, we obtain the top 5 place on crosslingual tasks.
%R 10.18653/v1/2022.semeval-1.186
%U https://aclanthology.org/2022.semeval-1.186/
%U https://doi.org/10.18653/v1/2022.semeval-1.186
%P 1336-1342
Markdown (Informal)
[ECNU_ICA at SemEval-2022 Task 10: A Simple and Unified Model for Monolingual and Crosslingual Structured Sentiment Analysis](https://aclanthology.org/2022.semeval-1.186/) (Zhang et al., SemEval 2022)
ACL