论文标题
隐喻的多义检测：传统的隐喻遇到单词sense剥夺歧义
Metaphorical Polysemy Detection: Conventional Metaphor meets Word Sense Disambiguation
论文作者
论文摘要
语言学家区分了新颖和常规的隐喻，这是NLP中的隐喻检测任务的区别。取而代之的是，无论隐喻类型如何，隐喻性被称为句子中令牌的属性。在本文中，我们研究了以这种方式处理常规隐喻的局限性，并主张我们将“隐喻多义检测”（MPD）命名的替代方案（MPD）。在MPD中，仅处理常规的隐喻性，并且它被称为词典中单词感官的特性。我们开发了第一个MPD模型，该模型学会识别英文WordNet中的常规隐喻。为了训练它，我们提出了一种新颖的培训程序，该程序将隐喻检测与单词sense删除（WSD）相结合。为了进行评估，我们手动在WordNet的两个子集中手动注释隐喻。我们的模型明显优于基线，基于最先进的隐喻检测模型，在其中一组中，ROC-AUC得分为0.78（比0.65）。此外，当与WSD模型配对时，我们的方法在识别文本中的常规隐喻（.659 F1中的常规隐喻与.626相比）中的最先进的隐喻检测模型优于最先进的隐喻检测模型。
Linguists distinguish between novel and conventional metaphor, a distinction which the metaphor detection task in NLP does not take into account. Instead, metaphoricity is formulated as a property of a token in a sentence, regardless of metaphor type. In this paper, we investigate the limitations of treating conventional metaphors in this way, and advocate for an alternative which we name 'metaphorical polysemy detection' (MPD). In MPD, only conventional metaphoricity is treated, and it is formulated as a property of word senses in a lexicon. We develop the first MPD model, which learns to identify conventional metaphors in the English WordNet. To train it, we present a novel training procedure that combines metaphor detection with word sense disambiguation (WSD). For evaluation, we manually annotate metaphor in two subsets of WordNet. Our model significantly outperforms a strong baseline based on a state-of-the-art metaphor detection model, attaining an ROC-AUC score of .78 (compared to .65) on one of the sets. Additionally, when paired with a WSD model, our approach outperforms a state-of-the-art metaphor detection model at identifying conventional metaphors in text (.659 F1 compared to .626).
