【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。更多还原

【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 aspectoriented 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.更多还原