Many language technology applications would benefit from the ability to
represent negation and its scope on top of widely-used linguistic resources. In
this paper, we investigate the possibility of obtaining a first-order logic
representation with negation scope marked using Universal Dependencies. To do
so, we enhance UDepLambda, a framework that converts dependency graphs to
logical forms. The resulting UDepLambda$\lnot$ is able to handle phenomena
related to scope by means of an higher-order type theory, relevant not only to
negation but also to universal quantification and other complex semantic
phenomena. The initial conversion we did for English is promising, in that one
can represent the scope of negation also in the presence of more complex
phenomena such as universal quantifiers.

通过 Universal Dependencies 标记 negation scope 的能力，实现 UDepLambda 和 higher-order type theory 结合，处理代表否定、全称量词等复杂语义现象。初步实现针对英语的转化表现良好。

利用通用依存关系生成带有否定范围的逻辑语句

Universal Dependencies to Logical Forms with Negation Scope

Universal Dependencies (UD) offer a uniform cross-lingual syntactic
representation, with the aim of advancing multilingual applications. Recent
work shows that semantic parsing can be accomplished by transforming syntactic
dependencies to logical forms. However, this work is limited to English, and
cannot process dependency graphs, which allow handling complex phenomena such
as control. In this work, we introduce UDepLambda, a semantic interface for UD,
which maps natural language to logical forms in an almost language-independent
fashion and can process dependency graphs. We perform experiments on question
answering against Freebase and provide German and Spanish translations of the
WebQuestions and GraphQuestions datasets to facilitate multilingual evaluation.
Results show that UDepLambda outperforms strong baselines across languages and
datasets. For English, it achieves a 4.9 F1 point improvement over the
state-of-the-art on GraphQuestions. Our code and data can be downloaded at
this https URL

UDepLambda 是一个语义接口，它将自然语言映射到逻辑形式，可以处理依存图，并在自然语言理解中表现出色，对于多语言问题回答有很好的性能。