Comparing Knowledge-Intensive and Data-Intensive Models for English Resource Semantic Parsing
| Autor: | Junjie Cao, Zi Lin, Weiwei Sun, Xiaojun Wan |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: | |
| Zdroj: | Computational Linguistics, Vol 47, Iss 1, Pp 43-68 (2021) |
| Druh dokumentu: | article |
| ISSN: | 0891-2017 1530-9312 |
| DOI: | 10.1162/coli_a_00395 |
| Popis: | AbstractIn 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. |
| Databáze: | Directory of Open Access Journals |
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