AzariyasMekonen/AMHARIC-ACOS

# Aspect-Category-Opinion-Sentiment (ACOS) Quadruple Extraction This repo contains the data sets and source code of our paper: Aspect-Category-Opinion-Sentiment Quadruple Extraction with Implicit Aspects and Opinions [[ACL 2021]](aclanthology.org). - We introduce a new ABSA task, named Aspect-Category-Opinion-Sentiment Quadruple (ACOS) Extraction, to extract fine-grained ABSA Quadruples from product reviews; - We construct two new datasets for the task, with ACOS quadruple annotations, and benchmark the task with four baseline systems; - Our task and datasets provide a good support for discovering implicit opinion targets and implicit opinion expressions in product reviews. ## Task The Aspect-Category-Opinion-Sentiment (ACOS) Quadruple Extraction aims to extract all aspect-category-opinion-sentiment quadruples, i.e., (aspect expression, aspect category, opinion expression, sentiment polarity), in a review sentence including implicit aspect and implicit opinion. ## Datasets Two new datasets, Restaurant-ACOS and Laptop-ACOS, are constructed for the ACOS Quadruple Extraction task: - Restaurant-ACOS is an extension of the existing SemEval Restaurant dataset, based on which we add the annotation of implicit aspects, implicit opinions, and the quadruples; - Laptop-ACOS is a brand new one collected from the Amazon Laptop domain. It has twice size of the SemEval Loptop dataset, and is annotated with quadruples containing all explicit/implicit aspects and opinions. The following table shows the comparison between our two ACOS Quadruple datasets and existing representative ABSA datasets. ## Methods We benchmark the ACOS Quadruple Extraction task with four baseline systems: - Double-Propagation-ACOS - JET-ACOS - TAS-BERT-ACOS - Extract-Classify-ACOS We provided the source code of Extract-Classify-ACOS. The source code of the other three methods will be provided soon. Overview of our Extract-Classify-ACOS method. The first step p …