Position-Aware Tagging for Aspect Sentiment Triplet Extraction

By Lu Xu, Hao Li, Wei Dang Lu, Lidong Bing. Conference on Empirical Methods in Natural Language Processing, 2020

328 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/30a713827f4a8b64e05ed3491fa6308482467583

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Lidong Bing
2. Wai Lam
3. Wenxuan Zhang
4. Xin Li
5. Yang Deng
6. Sinno Jialin Pan
7. Yifei Yuan
8. Chengwei Qin

In Information Systems

1. Wenxuan Zhang
2. Wai Lam
3. Sinno Jialin Pan
4. Lidong Bing
5. Le Sun
6. Shafiq R. Joty
7. Xianpei Han
8. Bing-Quan Liu

Citations in the corpus

• Aspect Sentiment Quad Prediction as Paraphrase Generation (2021, methodology): “We adopt two types of baselines: 1) pipeline-based methods including CMLA+ (Wang et al., 2017), Li-unified-R (Li et al., 2019a), Peng-pipeline (Peng et al., 2020) which firstly extract aspect and opinion terms separately, then conduct the pairing; Two-stage (Huang et al., 2021) which proposes a two-st”
• Sentiment Analysis in the Era of Large Language Models: A Reality Check (2023, methodology): “The datasets we utilized were introduced by Xu et al. (2020), which were built upon four UABSA datasets.”
• Is GPT-3 a Good Data Annotator? (2022, methodology): “For aspect sentiment triplet extraction, we use the laptop domain split released by (Xu et al., 2020).”
• Unified Structure Generation for Universal Information Extraction (2022, methodology): “Sentiment We conduct sentiment extraction experiments on the sentiment triplet extraction (Xu et al., 2020) of SemEval 14/15/16 aspect sentiment analysis datasets.”