Papers › Extracting Entities of Interest from Comparative Product Reviews
Extracting Entities of Interest from Comparative Product Reviews
Jatin Arora, Sumit Agrawal, Pawan Goyal, Sayan Pathak
This paper presents a deep learning based approach to extract product comparison information out of user reviews on various e-commerce websites. Any comparative product review has three major entities of information: the names of the products being compared, the user opinion (predicate) and the feature or aspect under comparison. All these informing entities are dependent on each other and bound by the rules of the language, in the review. We observe that their inter-dependencies can be captured well using LSTMs. We evaluate our system on existing manually labeled datasets and observe out-performance over the existing Semantic Role Labeling (SRL) framework popular for this task.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Predicate Detection | Product Reviews 2017 | Bidirectional-LSTM | F1 score | 50.4 | #1 of 2 | Archive leaderboard | report |
| Predicate Detection | Product Reviews 2017 | Semantic Role Labeling (SRL) | F1 score | 6.5 | #2 of 2 | Archive leaderboard | report |
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