Position-Aware Tagging for Aspect Sentiment Triplet Extraction

Abstract

Aspect Sentiment Triplet Extraction (ASTE)is the task of extracting the triplets of target entities, their associated sentiment, and opinion spans explaining the reason for the sentiment. Existing research efforts mostly solve this problem using pipeline approaches, which break the triplet extraction process into several stages. Our observation is that the three elements within a triplet are highly related to each other, and this motivates us to build a joint model to extract such triplets using a sequence tagging approach. However, how to effectively design a tagging approach to extract the triplets that can capture the rich interactions among the elements is a challenging research question. In this work, we propose the first end-to-end model with a novel positionaware tagging scheme that is capable of jointly extracting the triplets. Our experimental results on several existing datasets show that jointly capturing elements in the triplet using our approach leads to improved performance over the existing approaches. We also conducted extensive experiments to investigate the model effectiveness and robustness 1 .

Learning Span-Level Interactions for Aspect Sentiment Triplet Extraction

“…Our proposed Span-ASTE model is evaluated on four ASTE datasets released by Xu et al (2020b), which include three datasets in the restaurant domain and one dataset in the laptop domain. The first version of the ASTE datasets are released by Peng et al (2019).…”

“…Previous work by Zhang et al (2020) and Wu et al (2020) independently predict the sentiment relation for all possible word-word pairs, hence they require decoding heuristics to determine the overall sentiment polarity of a triplet. JET (Xu et al, 2020b) models the ASTE task as a structured prediction problem with a position-aware tagging scheme to capture the interaction of the three elements in a triplet.…”

“…Recent end-to-end approaches (Wu et al, 2020;Xu et al, 2020b;Zhang et al, 2020) can jointly extract the target and opinion terms and classify their sentiment relation. One drawback is that they heavily rely on word-to-word interactions to predict the sentiment relation for the target-opinion pair.…”

Introduction

“…Although these five components, in particular aspect and sentiment, have been discussed for nearly two decades now [5,8], they remain the focus of much active research [12,13] due to the wide variety of potential applications. Figure 1.1 shows an example of an opinion, in this case a product review from Amazon.…”

Aspect Sentiment Classification with Aspect-Specific Opinion Spans

“…Aspect Based Sentiment Analysis (ABSA) (Pang and Lee, 2008;Liu, 2012) is an extensively studied sentiment analysis task on a fine-grained semantic level, i.e., opinion targets explicitly mentioned in sentences. Previous ABSA studies focused on a few sub-tasks, such as Aspect Sentiment Classification (ASC) (Wang et al, 2016;Ma et al, 2018), Aspect Term Extraction (ATE) (Li et al, 2018b;He et al, 2017), Aspect and Opinion Co-Extraction (Liu et al, 2013;Xu et al, 2018;Dai and Song, 2019), E2E-ABSA (a joint task of ASC and ATE) (Li et al, 2019a;He et al, 2019;Li et al, 2019b), Aspect Sentiment Triplet Extraction (ASTE) (Peng et al, 2019;Xu et al, 2020), etc. ASC analyzes the sentiment polarity of given aspects/targets in a review.…”