Improving Aspect-Level Sentiment Analysis with Aspect Extraction
원문 초록
Aspect-based sentiment analysis (ABSA), a popular research area in NLP has two distinct parts -- aspect extraction (AE) and labeling the aspects with sentiment polarity (ALSA). Although distinct, these two tasks are highly correlated. The work primarily hypothesize that transferring knowledge from a pre-trained AE model can benefit the performance of ALSA models. Based on this hypothesis, word embeddings are obtained during AE and subsequently, feed that to the ALSA model. Empirically, this work show that the added information significantly improves the performance of three different baseline ALSA models on two distinct domains. This improvement also translates well across domains between AE and ALSA tasks.
함께 살펴볼 논문
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From Annotation to Adaptation: Metrics, Synthetic Data, and Aspect Extraction for Aspect-Based Sentiment Analysis with Large Language Models
Towards a One-stop Solution to Both Aspect Extraction and Sentiment Analysis Tasks with Neural Multi-task Learning
An Aspect Extraction Framework using Different Embedding Types, Learning Models, and Dependency Structure
M-ABSA: A Multilingual Dataset for Aspect-Based Sentiment Analysis
Multi-View Attention Syntactic Enhanced Graph Convolutional Network for Aspect-based Sentiment Analysis
Multi-Domain ABSA Conversation Dataset Generation via LLMs for Real-World Evaluation and Model Comparison
토론
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