Transformer-based Text Classification on Unified Bangla Multi-class Emotion Corpus
원문 초록
In this research, we propose a complete set of approaches for identifying and extracting emotions from Bangla texts. We provide a Bangla emotion classifier for six classes: anger, disgust, fear, joy, sadness, and surprise, from Bangla words using transformer-based models, which exhibit phenomenal results in recent days, especially for high-resource languages. The Unified Bangla Multi-class Emotion Corpus (UBMEC) is used to assess the performance of our models. UBMEC is created by combining two previously released manually labeled datasets of Bangla comments on six emotion classes with fresh manually labeled Bangla comments created by us. The corpus dataset and code we used in this work are publicly available.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
A Simple Ensemble Strategy for LLM Inference: Towards More Stable Text Classification
Linguistic Interpretability of Transformer-based Language Models: a systematic review
A thorough benchmark of automatic text classification: From traditional approaches to large language models
GNN-CNN: An Efficient Hybrid Model of Convolutional and Graph Neural Networks for Text Representation
Reliable Decision Support with LLMs: A Framework for Evaluating Consistency in Binary Text Classification Applications
Token Masking Improves Transformer-Based Text Classification
토론
이 논문과 연결된 포럼 글이 아직 없습니다.