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Text parsing of colonoscopy and pathology notes was performed using a PERL Regular Expression-based text parser implemented in SAS 9.3 for Windows (SAS Institute, Cary, NC), similar to that used by Hinchcliff et al. The total number of colonoscopies was evaluated by the above data collection parameters.
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Further options of representation include text parsing to consider additional information on terms such as word type or syntax [77].
All the text parsing and processing procedures were done using a series of OCAML programs.
All the text parsing and processing procedures were done using a series of programs coded in OCAML language.
Text parsing: Similar as done in tag mining (above).
Text parsing maps keywords or phrases to specific syndromes.
The papers are organized in topical sections on corpora and language resources, speech recognition, tagging, classification and parsing of text and speech, speech and spoken language generation, semantic processing of text and speech, integrating applications of text and speech processing, machine translation, automatic dialogue systems, multimodal techniques and modeling.
Sequence retrieval, BLAST scans, identification, validation and annotation of hits and all associated parsing of text files were carried out in batch mode with scripts written in Perl5 [87], using modules from the BioPerl library [88] and are available from the authors on request.
Perl was chosen because of its text-parsing and internet capabilities; and because it is widely available.
Computerized text-parsing tools such as LSA allow an objective review of text responses that would be otherwise impossible to standardize.
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).
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