Distilbert Base Uncased Finetuned Absa As

🚀 基於輔助句子進行方面情感分析（ABSA）的微調Distilbert模型

本項目是使用帶有輔助句子的Distilbert模型，針對方面情感分析（ABSA）任務進行微調的成果。該模型利用了NAVER提供的數據集，為CentraleSupélec自然語言處理課程而訓練。

🚀 快速開始

本模型是基於預訓練的Distilbert模型，通過構建輔助句子將方面情感分析（ABSA）任務轉化為句子對分類任務進行微調。

📚 詳細文檔

論文引用

@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.",
}

論文摘要

方面情感分析（ABSA）旨在識別針對特定方面的細粒度觀點極性，是情感分析（SA）中一項具有挑戰性的子任務。在本文中，作者從方面構建輔助句子，並將ABSA轉換為句子對分類任務，如問答（QA）和自然語言推理（NLI）。作者對來自BERT的預訓練模型進行微調，並在SentiHood和SemEval - 2014任務4數據集上取得了新的最先進成果。源代碼可在此處獲取。