In this work, we present a phenomenon-oriented comparative analysis of the two dominant approaches in English Resource Semantic (ERS) parsing: classic, knowledge-intensive and neural, data-intensive models. To reflect state-of-the-art neural NLP technologies, a factorization-based parser is introduced that can produce Elementary Dependency Structures much more accurately than previous data-driven parsers. We conduct a suite of tests for different linguistic phenomena to analyze the grammatical competence of different parsers, where we show that, despite comparable performance overall, knowledge- and data-intensive models produce different types of errors, in a way that can be explained by their theoretical properties. This analysis is beneficial to in-depth evaluation of several representative parsing techniques and leads to new directions for parser development.

在这项工作中，我们对英语资源语义（ERS）解析中的两种主要方法进行了面向现象的比较分析：经典，知识密集型和神经，数据密集型模型。为了反映最新的神经NLP技术，引入了基于因式分解的解析器，与以前的数据驱动解析器相比，该解析器可以更精确地生成基本依赖结构。我们针对不同的语言现象进行了一系列测试，以分析不同解析器的语法能力，其中我们表明，尽管总体表现相当，但知识和数据密集型模型仍会产生不同类型的错误，可以通过以下方式进行解释：它们的理论特性。