Dependency parsing is a tool widely used in the field of Natural language processing and computational linguistics. However, there is hardly any work that connects dependency parsing to monotonicity, which is an essential part of logic and linguistic semantics. In this paper, we present a system that automatically annotates monotonicity information based on Universal Dependency parse trees. Our system utilizes surface-level monotonicity facts about quantifiers, lexical items, and token-level polarity information. We compared our system's performance with existing systems in the literature, including NatLog and ccg2mono, on a small evaluation dataset. Results show that our system outperforms NatLog and ccg2mono.

翻译：依赖性分类是自然语言处理和计算语言领域广泛使用的一种工具,然而,几乎没有任何工作将依赖性分类与单一度(这是逻辑和语言语义学的一个基本部分)联系起来。在本文中,我们提出了一个系统,自动对基于普遍依赖性剖析树的单一度信息进行注解。我们的系统利用了地表层次关于量化物、词汇项目和象征性极地信息的单一度事实。我们比较了我们的系统绩效与文献中现有的系统,包括NatLog和ccg2mono,关于一个小的评估数据集。结果显示,我们的系统优于NatLog和ccg2mono。