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Flexibly utilizing syntactic knowledge in aspect-based sentiment analysis
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AI
DOI：10.1016/j.ipm.2023.103630.png)
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Aspect-based sentiment analysis (ABSA) refers to ascertaining the propensity of sentiment expressed in a text towards a particular aspect. While previous models have utilized dependency graphs and GNNs to facilitate information exchange, they face challenges such as smoothing of aspect representation and a gap between word-based dependency graphs and subwordbased BERT. Taking into account the above deficiencies, we argue for a new approach called SRE-BERT that flexibly utilizes syntax knowledge to enhance aspect representations by relying on syntax representations. First, we propose a syntax representation encoder to acquire the syntactic vector for each token. Then, we devise a syntax-guided transformer that employs syntax representation to compute multi-head attention, thereby enabling direct syntactic interaction between any two tokens. Finally, the token-level vectors derived from the syntax-guided transformer are employed to enhance the semantic representations obtained by BERT. In addition, during the aforementioned process, we introduced a Masked POS Label Prediction (MPLP) method to pre-train the syntax encoder. A wide range of studies have been undertaken on data collections covering three distinct fields, and the results indicate that our SRE-BERT outperforms the second-ranked model by 1.97%, 1.55%, and 1.20% on the Rest14, Lap14, and Twitter 3 datasets, respectively
Keyword:
Aspect-based sentiment analysis
BERT
Syntax representation
Syntax-guided transformer
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6.9
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5.2K
被引数:
1.4W
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Modeling label-wise syntax for fine-grained sentiment analysis of reviews via-based neural model
PREAI
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