论文标题
通过方面提取来改善方面级别的情感分析
Improving Aspect-Level Sentiment Analysis with Aspect Extraction
论文作者
论文摘要
基于方面的情感分析（ABSA）是NLP中的流行研究领域具有两个不同的部分 - 置换（AE），并用情感极性（ALSA）标记这些方面。尽管很明显，但这两个任务高度相关。这项工作主要假设从预训练的AE模型中转移知识可以使ALSA模型的性能受益。基于此假设，在AE期间获得了单词嵌入，随后将其馈送到ALSA模型中。从经验上讲，这项工作表明，增加的信息显着提高了两个不同领域上三种不同的基线ALSA模型的性能。这种改进还可以很好地转化为AE和ALSA任务之间的范围。
Aspect-based sentiment analysis (ABSA), a popular research area in NLP has two distinct parts -- aspect extraction (AE) and labeling the aspects with sentiment polarity (ALSA). Although distinct, these two tasks are highly correlated. The work primarily hypothesize that transferring knowledge from a pre-trained AE model can benefit the performance of ALSA models. Based on this hypothesis, word embeddings are obtained during AE and subsequently, feed that to the ALSA model. Empirically, this work show that the added information significantly improves the performance of three different baseline ALSA models on two distinct domains. This improvement also translates well across domains between AE and ALSA tasks.
