Position-Aware Tagging for Aspect Sentiment Triplet Extraction (2020)

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 position-aware 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.

## introduction designing effective algorithms that are capable of automatically performing sentiment analysis and opinion mining is a challenging and important task in the field of natural language processing @xcite @xcite @xcite . recently, aspect-based sentiment analysis @xcite or targeted sentiment analysis @xcite which focuses on extracting target phrases as well as the sentiment associated with each target, has been receiving much attention. in this work, we focus on a relatively new task -aspect sentiment triplet extraction (aste) proposed by @xcite . such a task is required to extract not only the targets and the sentiment mentioned above, but also the corresponding opinion spans expressing the sentiment for each target. such three elements: a target, its sentiment and the corresponding opinion span, form a triplet to be extracted. figure 1 presents an example sentence containing two targets in solid boxes. each target is associated with a sentiment, where we use + to denote the positive polarity, 0 for neutral, andfor negative. two opinion spans in dashed boxes are connected to their targets by arcs. such opinion spans are important, since they largely explain the sentiment polarities for the corresponding targets @xcite . this aste problem was basically untouched before, and the only existing work that we are aware of @xcite employs a 2-stage pipeline approach. at the first stage, they employ a unified tagging scheme which fuses the target tag based on the bioes foot_0 tagging scheme, and sentiment tag together. under such a unified tagging scheme, they proposed methods based on long short-term memory networks (lstm) @xcite , conditional random 0 + food was so so but excited to see many vegan options fields (crf) @xcite and graph convolutional networks (gcn) @xcite to perform sequence labeling to extract targets with sentiment as well as opinion spans. at the second stage, they use a classifier based on multi-layer perceptron (mlp) to pair each target (containing a sentiment label) with the corresponding opinion span to obtain all the valid triplets. one important observation is that the three elements in a triplet are highly related to each other. specifically, sentiment polarity is largely determined by an opinion span as well as the target and its context, and an opinion span also depends on the target phrase in terms of wording (e.g., an opinion span "fresh" usually describes food targets instead of service). such an observation implies that jointly capturing the rich interaction among three elements in a triplet might be a more effective approach. however, the bioes tagging scheme on which the existing approaches based comes with a severe limitation for this task: such a tagging scheme without encoding any positional information fails to specify the connection between a target and its opinion span as well as the rich interactions among the three elements due to the limited expressiveness. specifically, bioes uses the tag b or s to represent the beginning of a target. for example, in the example sentence in figure 1 , "vegan" should be labeled with b, but the tagging scheme does not contain any information to specify the position of its corresponding opinion "excited". using such a tagging scheme inevitably leads to an additional step to connect each target with an opinion span as the second stage in the pipeline approach. the skip-chain sequence models @xcite are able to capture interactions between given input tokens which can be far away from each other. however, they are not suitable for the aste task where the positions of targets and opinion spans are not explicitly provided but need to be learned. motivated by the above observations, we present a novel approach that is capable of predicting the triplets jointly for aste. specifically, we make the following contributions in this work: ## our approach our objective is to design a model jet to extract the triplet of target, target sentiment, and opinion span jointly. we first introduce the new position-aware tagging scheme, followed by the model architecture. we next present our simple lstm-based neural architecture for learning feature representations, followed by our method to calculate factorized feature scores based on such feature representations for better capturing the interactions among elements in a triplet. finally, we also discuss a variant of our model. ## conclusion in this work, we propose a novel position-aware tagging scheme by enriching label expressiveness to address a limitation associated with existing works. such a tagging scheme is able to specify the connection among three elements -a target, the target sentiment as well as an opinion span in an aspect sentiment triplet for the aste task. based on the position-aware tagging scheme, we propose a novel approach jet that is capable of jointly extracting the aspect sentiment triplets. we also design factorized feature representations so as to effectively capture the interaction. we conduct extensive experiments and results show that our models outperform strong baselines significantly with detailed analysis. future work includes finding applications of our novel tagging scheme in other tasks involving extracting triplets as well as extending our approach to support other tasks within sentiment analysis.