target-dependent情感分析与aspect-level情感分类类似。不同之处在于，target是明确出现在句子中的词，而aspect是更宽的一个概念，不一定出现在句子中。
entity是“颜色 ”输出：情感极性基于aspect词的情感分析（aspect-term sentiment analysis ——ATSA）:主...
In this paper, a graph-based semi-supervised learning approach for aspect term extraction is proposed. In this approach, every identified token in the review document is classified as aspect or non-aspect term from a small set of labeled tokens using label spreading algorithm. The k-Nearest ...
aims to extract explicit aspect term from reviewers’ expressed opinions. However, the distribution of samples containing different numbers of aspect terms is long-tailed. Due to the scarcity of long-tailed samples and the existence of multiple variable-length aspect terms inside each sample, most ...
Hence, the study deals with two main sub-tasks in ABSA, named Aspect Category Detection (ACD) and Aspect Term Extraction (ATE). In the first sub-task, aspects categories are extracted using topic modeling and filtered by an oracle further, and it is fed to zero-shot learning as the ...
analysis (ABSA) two subtasks:aspect-category sentiment analysis (ACSA)andaspect-termsentiment analysis (ATSA) 大多数先前的方法采用长期短期记忆和注意机制来预测相关目标的情绪极性,这通常是复杂的并且需要更多的训练时间。我们提出了一种基于卷积神经网络和门控机制的模型,该模型更加准确和高效。 数据集...
Aspect term extraction for opinion mining using a HierarchicalSelf-Attention NetworkAvinash Kumara, ⇑ ,1 , Aditya Srikanth Veerubhotla a,2 , Vishnu Teja Narapareddy a,3 , Vamshi Aruru a,4 ,Lalita Bhanu Murthy Netia,b,5 , Aruna Malapati a,6a Birla Institute of Technology & ...
9. Enhancing Aspect Term Extraction with Soft Prototypes论文阅读笔记,程序员大本营,技术文章内容聚合第一站。
We present a densely-connected neural network for the aspect term extraction task. It enables preserving feature information from the bottommost layer to the uppermost layer in deep neural networks. The experiment results on two standard benchmark ABSA datasets indicate that our model improves ATE pe...
1. Effective LSTMs for Target-Dependent Sentiment Classification with Long Short Term Memory Duyu Tang, Bing Qin, Xiaocheng Feng, Ting Liu Proceeding of the 26th International Conference on Computational Linguistics (COLING 2016, full paper)