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CoNLL-2014 Shared Task: Grammatical Error Correction
Description of Data Preprocessing Scripts
4 Aug 2014 Version 3.2
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Table of Contents
=================
1. General
2. Pre-requisites
3. Usage
1. General
==========
This README file describes the usage of scripts for preprocessing the NUCLE version 3.2 corpus.
Quickstart:
a. Regenerate the preprocessed files with full syntactic information:
% python preprocess.py -o nucle.sgml conllFileName annFileName m2FileName
b. Get tokenized annotations without syntactic information:
% python preprocess.py -l nucle.sgml conllFileName annFileName m2FileName
where
nucle.sgml - input SGML file
conllFileName - output file that contains pre-processed sentences in CoNLL format.
annFileName - output file that contains standoff error annotations.
m2FileName - output file that contains error annotations in the M2 scorer format.
2. Pre-requisites
=================
+ Python (2.6.4, other versions >= 2.6.4, < 3.0 might work but are not tested)
+ nltk (http://www.nltk.org, version 2.0b7, needed for sentence splitting and word tokenization)
+ Stanford parser (version 2.0.1, http://nlp.stanford.edu/software/stanford-parser-2012-03-09.tgz)
If you only use the scripts to generate error annotations needed by the M2 scorer, Stanford parser is not required.
Otherwise, "stanford-parser-2012-03-09" need to be in the same directory as "scripts".
3. Usage
========
Preprocessing the data from single annotation
Usage: python preprocess.py OPTIONS sgmlFileName conllFileName annotationFileName m2FileName
Where
sgmlFileName - NUCLE SGML file
conllFileName - output file name for pre-processed sentences in CoNLL format (e.g., conll14st-preprocessed.conll).
annotationFileName - output file name for error annotations (e.g., conll14st-preprocessed.conll.ann).
m2FileName - output file name in the M2 scorer format (e.g., conll14st-preprocessed.conll.m2).
OPTIONS
-o - output will contain POS tags and parse tree info (i.e., the same as the released preprocessed file, runs slowly).
-l - output will NOT contain POS tags and parse tree info (runs quickly).
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