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• W3216877746 abstract "Aspect-level sentiment analysis identifies the sentiment polarity of aspect terms in complex sentences, which is useful in a wide range of applications. It is a highly challenging task and attracts the attention of many researchers in the natural language processing field. In order to obtain a better aspect representation, a wide range of existing methods design complex attention mechanisms to establish the connection between entity words and their context. With the limited size of data collections in aspect-level sentiment analysis, mainly because of the high annotation workload, the risk of overfitting is greatly increased. In this paper, we propose a Shared Multitask Learning Network (SMLN), which jointly trains auxiliary tasks that are highly related to aspect-level sentiment analysis. Specifically, we use opinion term extraction due to its high correlation with the main task. Through a custom-designed Cross Interaction Unit (CIU), effective information of the opinion term extraction task is passed to the main task, with performance improvement in both directions. Experimental results on SemEval-2014 and SemEval-2015 datasets demonstrate the competitive performance of SMLN in comparison to baseline methods." @default.
• W3216877746 created "2021-12-06" @default.
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• W3216877746 date "2021-11-29" @default.
• W3216877746 modified "2023-10-15" @default.
• W3216877746 title "Multitask Learning for Aspect-Based Sentiment Classification" @default.
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• W3216877746 doi "https://doi.org/10.1155/2021/2055555" @default.
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