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- W4362647029 abstract "The accuracy of attribute word extraction directly affects the effects of aspect-based sentiment analysis. Traditional attribute word extraction methods disregard the impact of polysemy on extraction results. This paper proposes an attribute word extraction model based on RoBERTa-CRF. First, the RoBERTa model is used to encode each word of Chinese comment text. Then, in the Conditional Random Field layer, the relations between attribute words are learned using the transfer score matrix. The most logical prediction results in the paths are calculated, reducing the number of invalid labels and improving prediction accuracy. The test results on the SemEval-2016 Task dataset reveal that the RoBERTa-CRF model outperforms other comparison models by 2.2 % in terms of optimal results." @default.
- W4362647029 created "2023-04-07" @default.
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- W4362647029 date "2022-12-17" @default.
- W4362647029 modified "2023-09-24" @default.
- W4362647029 title "An Attribute Word Extraction Model Incorporating RoBERTa and CRF" @default.
- W4362647029 doi "https://doi.org/10.1109/hccs55241.2022.10090337" @default.
- W4362647029 hasPublicationYear "2022" @default.
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