论文标题
PYABSA：一个可再现基于方面情感分析的模块化框架
PyABSA: A Modularized Framework for Reproducible Aspect-based Sentiment Analysis
论文作者
论文摘要
基于方面的情感分析（ABSA）的进步敦促缺乏一个用户友好的框架，该框架在很大程度上可以降低复制最先进的ABSA表现的困难，尤其是对于初学者而言。为了满足需求，我们提出了一个模块化的框架，建立在Pytorch上，用于可再现的ABSA。为了促进ABSA研究，Pyabsa支持几个ABSA子任务，包括方面术语提取，方面情感分类和基于端到端的情感分析。具体而言，Pyabsa集成了29个型号和26个数据集。只有几行代码，可以再现特定数据集上的模型的结果。使用模块化的设计，Pyabsa也可以灵活地扩展到所考虑的模型，数据集和其他相关任务。此外，Pyabsa强调了其数据的增强和注释功能，从而显着解决数据稀缺性。欢迎所有人尝试\ url {https://github.com/yangheng95/pyabsa}。
The advancement of aspect-based sentiment analysis (ABSA) has urged the lack of a user-friendly framework that can largely lower the difficulty of reproducing state-of-the-art ABSA performance, especially for beginners. To meet the demand, we present \our, a modularized framework built on PyTorch for reproducible ABSA. To facilitate ABSA research, PyABSA supports several ABSA subtasks, including aspect term extraction, aspect sentiment classification, and end-to-end aspect-based sentiment analysis. Concretely, PyABSA integrates 29 models and 26 datasets. With just a few lines of code, the result of a model on a specific dataset can be reproduced. With a modularized design, PyABSA can also be flexibly extended to considered models, datasets, and other related tasks. Besides, PyABSA highlights its data augmentation and annotation features, which significantly address data scarcity. All are welcome to have a try at \url{https://github.com/yangheng95/PyABSA}.
