Summary of Kpc-cf: Aspect-based Sentiment Analysis Via Implicit-feature Alignment with Corpus Filtering, by Kibeom Nam
KPC-cF: Aspect-Based Sentiment Analysis via Implicit-Feature Alignment with Corpus Filtering
by Kibeom Nam
First submitted to arxiv on: 29 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposed framework optimizes Aspect-Based Sentiment Analysis (ABSA) for Korean industrial reviews by integrating translated benchmark data with unlabeled Korean data. The model is fine-tuned on translated data to pseudo-label the actual Korean NLI set. LaBSE and MSP-based filtering are applied as implicit features, enhancing aspect category detection and polarity determination through additional training. This approach bridges dataset gaps and facilitates feature alignment with minimal resources. By leveraging high-resource datasets, reliable predictive models can be developed for corporate or individual communities in low-resource language countries. Compared to English ABSA, the framework showed an approximately 3% difference in F1 scores and accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Our research focuses on improving Aspect-Based Sentiment Analysis (ABSA) for Korean industrial reviews. Right now, there isn’t much information available about this topic. We created a new way to do ABSA in Korean that is easy to use and effective. We did this by combining translated data with Korean data we didn’t label yet. Our model was trained on the translated data so it could predict what the actual Korean data would look like. Then, we used LaBSE and MSP-based filtering as extra features to make our predictions better. This helped us align our data and use minimal resources. We hope that by sharing our approach and code, others can develop reliable models for Korean ABSA. |
Keywords
» Artificial intelligence » Alignment
