Distilbert Base Uncased Finetuned Absa As

🚀 基于辅助句子进行方面情感分析（ABSA）的微调Distilbert模型

本项目是使用带有辅助句子的Distilbert模型，针对方面情感分析（ABSA）任务进行微调的成果。该模型利用了NAVER提供的数据集，为CentraleSupélec自然语言处理课程而训练。

🚀 快速开始

本模型是基于预训练的Distilbert模型，通过构建辅助句子将方面情感分析（ABSA）任务转化为句子对分类任务进行微调。

📚 详细文档

论文引用

@inproceedings{sun-etal-2019-utilizing,
    title = "Utilizing {BERT} for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence",
    author = "Sun, Chi  and
      Huang, Luyao  and
      Qiu, Xipeng",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1035",
    doi = "10.18653/v1/N19-1035",
    pages = "380--385",
    abstract = "Aspect-based sentiment analysis (ABSA), which aims to identify fine-grained opinion polarity towards a specific aspect, is a challenging subtask of sentiment analysis (SA). In this paper, we construct an auxiliary sentence from the aspect and convert ABSA to a sentence-pair classification task, such as question answering (QA) and natural language inference (NLI). We fine-tune the pre-trained model from BERT and achieve new state-of-the-art results on SentiHood and SemEval-2014 Task 4 datasets. The source codes are available at https://github.com/HSLCY/ABSA-BERT-pair.",
}

论文摘要

方面情感分析（ABSA）旨在识别针对特定方面的细粒度观点极性，是情感分析（SA）中一项具有挑战性的子任务。在本文中，作者从方面构建辅助句子，并将ABSA转换为句子对分类任务，如问答（QA）和自然语言推理（NLI）。作者对来自BERT的预训练模型进行微调，并在SentiHood和SemEval - 2014任务4数据集上取得了新的最先进成果。源代码可在此处获取。