Multi-Task Learning Model Based on BERT and Knowledge Graph for Aspect-Based Sentiment Analysis
نویسندگان
چکیده
Aspect-based sentiment analysis (ABSA) aims to identify the of an aspect in a given sentence and thus can provide people with comprehensive information. However, many conventional methods need help discover linguistic knowledge implicit sentences. Additionally, they are susceptible unrelated words. To improve performance model ABSA task, multi-task based on Bidirectional Encoder Representation from Transformers (BERT) Knowledge Graph (SABKG) is proposed this paper. Expressly, part-of-speech information incorporated into output representation BERT, thereby obtaining textual semantic through knowledge. It also enhances terms. Moreover, paper constructs graph uses neural network learn embeddings triplet “aspect word, polarity, word”. The constructed improves contextual relationship between text’s experimental results three open datasets show that achieve most advanced compared previous models.
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ژورنال
عنوان ژورنال: Electronics
سال: 2023
ISSN: ['2079-9292']
DOI: https://doi.org/10.3390/electronics12030737