# Aspect Sentiment Triplet Extraction Based on Deep Relationship Enhancement Networks
## 2 Review of Literature  
ABSA (aspect-based sentiment analysis) is a fine-grained sentiment analysis that involves three sequential tasks: aspect extraction, opinion extraction, and sentiment polarity determination for aspect-opinion pairs. While existing works often focus on individual tasks such as sentiment polarity with aspect words specified, there are also some works on AOTE (Aspect and Opinion Term Co-Extraction). However, the identification of aspect words, corresponding opinion words, and sentiment expressed by those opinion words proposed in this paper belongs to a sequential task of aspect words, opinion sub-term, and sentiment polarity judgement, and ASTE needs to be accomplished at once; therefore, this section will provide a detailed description of these three inter-related tasks. Since the introduction of deep neural networks for ABSA [4], numerous studies have explored the application of deep neural networks to determine the sentiment orientation of aspect words in text. [PERSON] et al. [5] proposed using LSTM (Long Short-Term Memory) combined with attention mechanism to address this task, and [PERSON] et al. [6] proposed an interactive attention mechanism to assess the importance of words expressing aspect words in comments, while [PERSON] et al. [7] employed a collaborative attention mechanism to deepen the interaction between aspect words and their contexts. [PERSON] et al. [13] argued that when both aspect words and contexts are long, the simplicity of the weighted sum attention mechanism would introduce some noise; thus, they proposed a multi-granularity attention mechanism to reduce irrelevant information. Since the application of BERT and GCN in the field of NLP, a large number of works based on BERT and GCN have also studied ABSA, such as [PERSON] et al. [10] and [PERSON] et al. [14]. In the AOTE task, [PERSON] et al. [15] used the high-level semantic representation of each term in its context through dependency syntax and recursive neural network to extract aspect words and opinion words, the aspect words were associated with the opinion words, and, finally, the aspect words and opinion words in the sentence were identified through CRF (Conditional Random Fields). [PERSON] and [PERSON] [16] proposed using BiLSTM-CRF (Bidirectional Long Short-Term Memory Conditional Random Fields) to extract aspect words and opinion words. [PERSON] et al. [17] argued that syntax-based methods exhibit low accuracy when applied to unstructured text, and methods relying on the HMM (Hidden Markov Model) and CRF require a large number of manual labels. Therefore, a coupled attention mechanism is proposed to extract opinion words and aspect words. This mechanism necessitates the use of two separate attention mechanisms for each sentence to capture the direct and indirect relationships between aspect and opinion words. In addressing the challenge of extracting aspect words and opinion words across domains, [PERSON] and [PERSON] [18] proposed an Interaction Memory Network featuring local and global memory units, which enabled aspect words and opinion words within the same domain to interact with each other, as well as facilitating interaction between aspect words and opinion words across different domains. Consequently, this approach achieved higher experimental results in the extraction of aspect words and opinion words across domains, eliminating the need for additional resources. [PERSON] et al. (2019) stated that there are certain difficulties in extracting aspect words and opinion words, such as the fact that the relationships between aspect words and opinion words can be one-to-many, many-to-one, or even overlapping, while the extraction of aspect words and opinion words is interdependent rather than independent; therefore, when extracting these two types of entities, it is necessary to synchronously determine the relationship between entities.