Summary of Oats: Opinion Aspect Target Sentiment Quadruple Extraction Dataset For Aspect-based Sentiment Analysis, by Siva Uday Sampreeth Chebolu and Franck Dernoncourt and Nedim Lipka and Thamar Solorio
OATS: Opinion Aspect Target Sentiment Quadruple Extraction Dataset for Aspect-Based Sentiment Analysis
by Siva Uday Sampreeth Chebolu, Franck Dernoncourt, Nedim Lipka, Thamar Solorio
First submitted to arxiv on: 23 Sep 2023
Categories
- Main: Computation and Language (cs.CL)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces a new dataset, OATS, which aims to address the limitations of existing benchmark datasets for aspect-based sentiment analysis (ABSA). ABSA is a task that involves analyzing user-generated reviews to determine the target entity being reviewed, the high-level aspect to which it belongs, the sentiment words used to express the opinion, and the overall sentiment expressed. The OATS dataset consists of 27,470 sentence-level quadruples and 17,092 review-level tuples, covering three fresh domains. The authors conducted experiments using initial baselines, hoping to provide a more comprehensive exploration of ABSA. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new dataset for understanding people’s feelings about specific things in their reviews. It tries to figure out what they are talking about, the main topic they are discussing, and how they feel about it. Right now, most datasets used for this task focus on familiar topics like restaurants or computers, but there is not much data available for more complex tasks. This new dataset, OATS, aims to fill those gaps by providing a lot of examples from three new areas. |
