We examine the binary classification of sentiment views for verbal multiword expressions (MWEs). Sentiment views denote the perspective of the holder of some opinion. We distinguish between MWEs conveying the view of the speaker of the utterance (e.g., in “The company reinvented the wheel” the holder is the implicit speaker who criticizes the company for creating something already existing) and MWEs conveying the view of explicit entities participating in an opinion event (e.g., in “Peter threw in the towel” the holder is Peter having given up something). The task has so far been examined on unigram opinion words. Since many features found effective for unigrams are not usable for MWEs, we propose novel ones taking into account the internal structure of MWEs, a unigram sentiment-view lexicon and various information from Wiktionary. We also examine distributional methods and show that the corpus on which a representation is induced has a notable impact on the classification. We perform an extrinsic evaluation in the task of opinion holder extraction and show that the learnt knowledge also improves a state-of-the-art classifier trained on BERT. Sentiment-view classification is typically framed as a task in which only little labeled training data are available. As in the case of unigrams, we show that for MWEs a feature-based approach beats state-of-the-art generic methods.

我们研究了言语多词表达（MWE）的情感视图的二元分类。情绪观点表示某些观点持有者的观点。我们区分表达说话者观点的 MWE（例如，在“公司重新发明了轮子”中，持有者是批评公司创造现有事物的隐含说话者）和传递参与参与的显式实体观点的 MWE。一个意见事件（例如，在“彼得认输”中，持有者是彼得放弃了某些东西）。到目前为止，该任务已经在一元词组意见词上进行了检查。由于许多对一元组有效的功能不适用于 MWE，因此我们考虑到 MWE 的内部结构、一元情感视图词典和维基词典中的各种信息，提出了新颖的功能。我们还研究了分布方法，并表明归纳表示的语料库对分类有显着影响。我们在意见持有者提取任务中进行了外部评估，并表明所学到的知识还改进了在 BERT 上训练的最先进的分类器。情感视图分类通常被构建为一项任务，其中只有很少的标记训练数据可用。与一元组的情况一样，我们表明对于 MWE，基于特征的方法胜过最先进的通用方法。