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Extract Aspect
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Aspect extraction is the task of identifying and extracting terms relevant for opinion mining and sentiment analysis, for example terms for product attributes or features.
논문
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Boundary-Driven Table-Filling with Cross-Granularity Contrastive Learning for Aspect Sentiment Triplet Extraction
Exploring Large Language Models for Multimodal Sentiment Analysis: Challenges, Benchmarks, and Future Directions
RNG: Reducing Multi-level Noise and Multi-grained Semantic Gap for Joint Multimodal Aspect-Sentiment Analysis
AspectMMKG: A Multi-modal Knowledge Graph with Aspect-aware Entities
Chinese Financial Text Emotion Mining: GCGTS -- A Character Relationship-based Approach for Simultaneous Aspect-Opinion Pair Extraction
MvP: Multi-view Prompting Improves Aspect Sentiment Tuple Prediction
A Better Choice: Entire-space Datasets for Aspect Sentiment Triplet Extraction
Aspect-Sentiment-Multiple-Opinion Triplet Extraction
Extractive Opinion Summarization in Quantized Transformer Spaces
Aspect Based Sentiment Analysis with Aspect-Specific Opinion Spans
A Multi-task Learning Framework for Opinion Triplet Extraction
An Interpretable and Uncertainty Aware Multi-Task Framework for Multi-Aspect Sentiment Analysis
Conditional Augmentation for Aspect Term Extraction via Masked Sequence-to-Sequence Generation
Aspect and Opinion Term Extraction for Hotel Reviews using Transfer Learning and Auxiliary Labels
Aspect and Opinion Terms Extraction Using Double Embeddings and Attention Mechanism for Indonesian Hotel Reviews
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