《计算机应用》唯一官方网站 ›› 2023, Vol. 43 ›› Issue (5): 1454-1460.DOI: 10.11772/j.issn.1001-9081.2022040502

• 人工智能 • 上一篇    下一篇

面向方面的自适应跨度特征的细粒度意见元组提取

陈林颖1,2, 刘建华1,2(), 孙水华1,2, 郑智雄1,2, 林鸿辉1,2, 林杰1,2   

  1. 1.福建工程学院 计算机科学与数学学院,福州 350118
    2.福建省大数据挖掘与应用技术重点实验室(福建工程学院),福州 350118
  • 收稿日期:2022-04-19 修回日期:2022-06-06 接受日期:2022-06-09 发布日期:2022-07-01 出版日期:2023-05-10
  • 通讯作者: 刘建华
  • 作者简介:陈林颖(1999—),女,福建莆田人,硕士研究生,CCF会员,主要研究方向:自然语言处理
    刘建华(1967—),男,江西吉安人,教授,博士,CCF会员,主要研究方向:智能计算、机器学习 656095080@qq.com
    孙水华(1962—),女,福建宁德人,教授,博士,CCF会员,主要研究方向:自然语言处理、机器翻译
    郑智雄(1996—),男,福建莆田人,硕士研究生,CCF会员,主要研究方向:自然语言处理
    林鸿辉(1996—),男,福建福州人,硕士研究生,CCF会员,主要研究方向:自然语言处理
    林杰(1999—),男,福建宁德人,硕士研究生,主要研究方向:自然语言处理。
  • 基金资助:
    国家自然科学基金资助项目(62172095);福建省自然科学基金资助项目(2019J01061137);福州市科技创新平台项目(2021?P?052)

Aspect-oriented fine-grained opinion tuple extraction with adaptive span features

Linying CHEN1,2, Jianhua LIU1,2(), Shuihua SUN1,2, Zhixiong ZHENG1,2, Honghui LIN1,2, Jie LIN1,2   

  1. 1.College of Information Science and Engineering,Fujian University of Technology,Fuzhou Fujian 350118,China
    2.Fujian Provincial Key Laboratory of Big Data Mining and Applications (Fujian University of Technology),Fuzhou Fujian 350118,China
  • Received:2022-04-19 Revised:2022-06-06 Accepted:2022-06-09 Online:2022-07-01 Published:2023-05-10
  • Contact: Jianhua LIU
  • About author:CHEN Linying, born in 1999, M. S. candidate. Her research interests include natural language processing.
    LIU Jianhua, born in 1967, Ph. D., professor. His research interests include intelligent computing, machine learning.
    SUN Shuihua, born in 1962, Ph. D., professor. Her research interests include natural language processing, machine translation.
    ZHENG Zhixiong, born in 1996, M. S. candidate. His research interests include natural language processing.
    LIN Honghui, born in 1996, M. S. candidate. His research interests include natural language processing.
    LIN Jie, born in 1999, M. S. candidate. His research interests include natural language processing.
  • Supported by:
    National Natural Science Foundation of China(62172095);Fujian Provincial Natural Science Foundation(2019J01061137);Fuzhou Science and Technology Innovation Platform Program(2021-P-052)

摘要:

面向方面的细粒度意见提取(AFOE)以意见对的形式从评论中提取方面词和意见词,或在此基础上再提取方面词的情感极性形成意见三元组。针对现有研究方法忽略了意见对与上下文相关性的问题,提出一种面向方面的自适应跨度特征的网格标记方案(ASF-GTS)模型。首先,利用BERT(Bidirectional Encode Representation from Transformers)模型获得句子的特征表示;然后,采用自适应跨度特征(ASF)方法加强意见对与局部上下文的联系;其次,通过网格标记方案(GTS)将意见对提取(OPE)转化为统一的网格标记任务;最后,使用特定的解码策略生成对应的意见对或意见三元组。在适用于意见元组提取任务的四个AFOE基准数据集上进行实验,结果表明,与GTS-BERT(Grid Tagging Scheme-BERT)模型相比,所提模型在意见对和意见三元组任务上的F1值分别提高了2.42%~7.30%和2.62%~6.61%。所提模型能够有效保留意见对与上下文的情感联系,更精确地提取意见对及其情感极性。

关键词: 网格标记方案, 方面词, 意见词, 意见对提取, 意见三元组提取, 面向方面的细粒度意见提取

Abstract:

Aspect-oriented Fine-grained Opinion Extraction (AFOE) extracts aspect terms and opinion terms from reviews in the form of opinion pairs or additionally extracts sentiment polarities of aspect terms on the basis of the above to form opinion triplets. Aiming at the problem of neglecting correlation between the opinion pairs and contexts, an aspect-oriented Adaptive Span Feature-Grid Tagging Scheme (ASF-GTS) model was proposed. Firstly, BERT (Bidirectional Encode Representation from Transformers) model was used to obtain the feature representation of the sentence. Then, the correlation between the opinion pair and local context was enhanced by the Adaptive Span Feature (ASF) method. Next, Opinion Pair Extraction (OPE) was transformed into a uniform grid tagging task by Grid Tagging Scheme (GTS). Finally, the corresponding opinion pairs or opinion triplet were generated by the specific decoding strategy. Experiments were carried out on four AFOE benchmark datasets adaptive to the task of opinion tuple extraction. The results show that compared with GTS-BERT (Grid Tagging Scheme-BERT) model, the proposed model has the F1-score improved by 2.42% to 7.30% and 2.62% to 6.61% on opinion pair or opinion triplet tasks, respectively. The proposed model can effectively reserve the sentiment correlation between opinion pair and context, and extract opinion pairs and their sentiment polarities more accurately.

Key words: Grid Tagging Scheme (GTS), aspect term, opinion term, Opinion Pair Extraction (OPE), Opinion Triplet Extraction (OTE), Aspect-oriented Fine-grained Opinion Extraction (AFOE)

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