%0 Journal Article %T Comparing Knowledge-Intensive and Data-Intensive Models for English Resource Semantic Parsing %A Cao, Junjie %A Lin, Zi %A Sun, Weiwei %A Wan, Xiaojun %J Computational Linguistics %D 2021 %8 March %V 47 %N 1 %I MIT Press %C Cambridge, MA %F cao-etal-2021-comparing %X 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. %R 10.1162/coli_a_00395 %U https://aclanthology.org/2021.cl-1.3 %U https://doi.org/10.1162/coli_a_00395 %P 43-68