"parsing accuracy" is an appropriate phrase to use in written English.
It is typically used when referring to the accuracy of a computer's performance in understanding or analyzing a task. For example: "The software's parsing accuracy was impressive, with 98% success rate."
First of all, parsing accuracy, as measured by the correct assignment of parts of speech to words, does not increase significantly when parsers select the tags themselves.
Possibly construction grammars (e.g., Goldberg 2003), which emphasize the coupling between the entrenched patterns of language (including ordinary phrase structure, clichés, and idioms) and their meanings and discourse function, will provide a conceptual basis for building statistical models of language that are sufficiently accurate to enable more nearly human-like parsing accuracy.
This parser achieved the highest published parsing accuracy on the GENIA Treebank (Tateisi et al., 2005) and is thus arguably the best parser available for text in PubMed abstracts (McClosky, 2009).
Under a Sparseval criterion, it is shown that including alternative word-sequence hypotheses has a much greater impact on parse accuracy than including alternate parse hypotheses.
Training the parser with only 100 sentences results in parse accuracy of about 72.5%.
This is supported by the strong correlation between parse accuracy and PPI accuracy, shown in Figure 11.
This figure demonstrates that increasing the size of the parser training set contributes to increasing parse accuracy.
> Figure 10 shows the parse accuracy for KSDEP and the accuracy of PPI extraction, when the parser is trained with varying amounts of data.
In addition to investigating the impact of different parsers and different syntactic representations on PPI identification accuracy, we also examine how the parse accuracy of a single parser affects the PPI accuracy.
Dr. Lowe working with Bill Hersh reported the accuracy of parsing radiology reports using the Sapphire indexing system [ 6].
Recent research shows that the accuracy for parsing of biomedical text is now in the 80 90% range (Clegg and Shepherd, 2007; Pyysalo et al., 2007b; Sagae et al., 2008a).
