Natural language parsing, as one of the central tasks in natural language processing, is widely used in many AI fields. In this paper, we address an issue of parser performance evaluation, particularly its variation across datasets. We propose three simple statistical measures to characterize the datasets and also evaluate their correlation to the parser performance. The results clearly show that different parsers have different performance variation and sensitivity against these measures. The method can be used to guide the choice of natural language parsers for new domain applications, as well as systematic combination for better parsing accuracy. © 2009 Springer Berlin Heidelberg.
CITATION STYLE
Zhang, Y., & Wang, R. (2009). Correlating natural language parser performance with statistical measures of the text. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5803 LNAI, pp. 217–224). https://doi.org/10.1007/978-3-642-04617-9_28
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