论文标题
潜在意见转移网络用于目标意见单词提取
Latent Opinions Transfer Network for Target-Oriented Opinion Words Extraction
论文作者
论文摘要
面向目标的意见单词提取（TOWE）是ABSA的新子任务，旨在提取句子中给定意见目标的相应意见词。最近，神经网络方法已应用于此任务并实现了令人鼓舞的结果。但是，注释的困难导致TOWE的数据集不足，这严重限制了神经模型的性能。相比之下，在线评论网站上很容易获得丰富的评论情感分类数据。这些评论包含大量潜在意见信息和语义模式。在本文中，我们提出了一个新颖的模型，将这些意见知识从资源丰富的评论情感分类数据集转移到低资源的任务拖曳。为了应对转移过程中的挑战，我们设计了一种有效的转换方法来获得潜在的意见，然后将它们整合到TOWE中。广泛的实验结果表明，与其他最先进的方法相比，我们的模型可以实现更好的性能，并且在不转移意见知识的情况下明显优于基本模型。进一步分析验证了我们的模型的有效性。
Target-oriented opinion words extraction (TOWE) is a new subtask of ABSA, which aims to extract the corresponding opinion words for a given opinion target in a sentence. Recently, neural network methods have been applied to this task and achieve promising results. However, the difficulty of annotation causes the datasets of TOWE to be insufficient, which heavily limits the performance of neural models. By contrast, abundant review sentiment classification data are easily available at online review sites. These reviews contain substantial latent opinions information and semantic patterns. In this paper, we propose a novel model to transfer these opinions knowledge from resource-rich review sentiment classification datasets to low-resource task TOWE. To address the challenges in the transfer process, we design an effective transformation method to obtain latent opinions, then integrate them into TOWE. Extensive experimental results show that our model achieves better performance compared to other state-of-the-art methods and significantly outperforms the base model without transferring opinions knowledge. Further analysis validates the effectiveness of our model.
