Papers › Recursive Neural Conditional Random Fields for Aspect-based Sentiment Analysis
Recursive Neural Conditional Random Fields for Aspect-based Sentiment Analysis
Wenya Wang, Sinno Jialin Pan, Daniel Dahlmeier, Xiaokui Xiao
In aspect-based sentiment analysis, extracting aspect terms along with the opinions being expressed from user-generated content is one of the most important subtasks. Previous studies have shown that exploiting connections between aspect and opinion terms is promising for this task. In this paper, we propose a novel joint model that integrates recursive neural networks and conditional random fields into a unified framework for explicit aspect and opinion terms co-extraction. The proposed model learns high-level discriminative features and double propagate information between aspect and opinion terms, simultaneously. Moreover, it is flexible to incorporate hand-crafted features into the proposed model to further boost its information extraction performance. Experimental results on the SemEval Challenge 2014 dataset show the superiority of our proposed model over several baseline methods as well as the winning systems of the challenge.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Aspect-Based Sentiment Analysis (ABSA) | SemEval 2014 Task 4 Sub Task 1 | RNCRF | Laptop (F1) | 78.42 | #4 of 4 | Archive leaderboard | report |
| Aspect-Based Sentiment Analysis (ABSA) | SemEval 2014 Task 4 Sub Task 1 | RNCRF | Restaurant (F1) | 69.74 | #4 of 4 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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