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KPC-cF: Aspect-Based Sentiment Analysis via Implicit-Feature Alignment with Corpus Filtering

arXiv 2407.00342 ↗First posted 29 Jun 2024cs.CL cs.AIcited by 0

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Abstract (arXiv)

Investigations into Aspect-Based Sentiment Analysis (ABSA) for Korean industrial reviews are notably lacking in the existing literature. Our research proposes an intuitive and effective framework for ABSA in low-resource languages such as Korean. It optimizes prediction labels by integrating translated benchmark and unlabeled Korean data. Using a model fine-tuned on translated data, we pseudo-labeled the actual Korean NLI set. Subsequently, we applied LaBSE and MSP-based filtering to this pseudo-NLI set as implicit feature, enhancing Aspect Category Detection and Polarity determination through additional training. Incorporating dual filtering, this model bridged dataset gaps and facilitates feature alignment with minimal resources. By implementing alignment pipelines, our approach aims to leverage high-resource datasets to develop reliable predictive and refined models within corporate or individual communities in low-resource language countries. Compared to English ABSA, our framework showed an approximately 3% difference in F1 scores and accuracy. We will release our dataset and code for Korean ABSA, at this link.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 40 of its 67 references that are in the map. First posted on arXiv 29 Jun 2024.

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40 of 67 references.

References · 40

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28 more
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