Comparing Knowledge-Intensive and Data-Intensive Models for English Resource Semantic Parsing.
In: Computational Linguistics, Jg. 47 (2021-03-01), Heft 1, S. 43-68
Online
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Zugriff:
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. [ABSTRACT FROM AUTHOR]
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Titel: |
Comparing Knowledge-Intensive and Data-Intensive Models for English Resource Semantic Parsing.
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Autor/in / Beteiligte Person: | Cao, Junjie ; Lin, Zi ; Sun, Weiwei ; Wan, Xiaojun |
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Zeitschrift: | Computational Linguistics, Jg. 47 (2021-03-01), Heft 1, S. 43-68 |
Veröffentlichung: | 2021 |
Medientyp: | academicJournal |
ISSN: | 0891-2017 (print) |
DOI: | 10.1162/coli_a_00395 |
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