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PyABSA: Open Framework for Aspect-based Sentiment Analysis

2022-08-02 11:27:36
Heng Yang, Ke Li
arXiv_CL
             
Abstract

Aspect-based sentiment analysis (ABSA) has become a prevalent task in recent years. However, the absence of a unified framework in the present ABSA research makes it challenging to compare different models' performance fairly. Therefore, we created an open-source ABSA framework, namely PYABSA. Besides, previous efforts usually neglect the precursor aspect term extraction (ASC) subtask and focus on the aspect sentiment classification (ATE) subtask. Compared to previous works, PYABSA includes the features of aspect term extraction, aspect sentiment classification, and text classification, while multiple ABSA subtasks can be adapted to PYABSA owing to its modular architecture. To facilitate ABSA applications, PYABSAseamless integrates multilingual modelling, automated dataset annotation, etc., which are helpful in deploying ABSA services. In ASC and ATE, PYABSA provides up to 33 and 7 built-in models, respectively, while all the models provide quick training and instant inference. Besides, PYABSA contains 180K+ ABSA instances from 21 augmented ABSA datasets for applications and studies. PyABSA is available at this https URL

Abstract (translated)

URL

https://arxiv.org/abs/2208.01368

PDF

https://arxiv.org/pdf/2208.01368.pdf

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