论文标题
培训整个空间模型，以提取目标意见单词
Training Entire-Space Models 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）是基于方面情感分析（ABSA）的子任务。鉴于句子中发生的句子和方面术语，Towe提取了该方面术语的相应意见词。 Towe有两种实例。在第一种类型中，方面术语至少与一个意见词相关联，而在第二种类型中，方面术语没有相应的意见单词。但是，先前的研究仅使用第一种实例训练和评估了他们的模型，从而导致样本选择偏差问题。具体而言，Towe模型仅使用第一种实例进行训练，而这些模型将使用第一种实例和第二种类型的实例来推断整个空间。因此，概括性能将受到伤害。此外，这些模型在第一种实例上的性能无法反映其在整个空间上的性能。为了验证样本选择偏见问题，四个流行的TOW数据集仅包含至少一个与一个意见单词相关的方面术语，还包括不相应意见单词的方面术语。这些数据集的实验结果表明，整个空间上的训练拖曳模型将显着提高模型性能，并且仅在第一种实例上评估TOWE模型将高估模型性能。
Target-oriented opinion words extraction (TOWE) is a subtask of aspect-based sentiment analysis (ABSA). Given a sentence and an aspect term occurring in the sentence, TOWE extracts the corresponding opinion words for the aspect term. TOWE has two types of instance. In the first type, aspect terms are associated with at least one opinion word, while in the second type, aspect terms do not have corresponding opinion words. However, previous researches trained and evaluated their models with only the first type of instance, resulting in a sample selection bias problem. Specifically, TOWE models were trained with only the first type of instance, while these models would be utilized to make inference on the entire space with both the first type of instance and the second type of instance. Thus, the generalization performance will be hurt. Moreover, the performance of these models on the first type of instance cannot reflect their performance on entire space. To validate the sample selection bias problem, four popular TOWE datasets containing only aspect terms associated with at least one opinion word are extended and additionally include aspect terms without corresponding opinion words. Experimental results on these datasets show that training TOWE models on entire space will significantly improve model performance and evaluating TOWE models only on the first type of instance will overestimate model performance.
