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Goal
====
	-> SCA-Extractor is a tool for information extraction in scientific papers of the biomedical domain which encompasses two main phases: classification & identification of relevant information.
a tool for sentence slassification which encompasses two main phases: setting filter and algorithm, and model use.
	-> Phase 1: Classification
		-> Use a classification model based on Weka API to classify sentence
	-> Phase 2: Identification of Relevant Information
		-> Extract information of each classified sentence using two techniques: Dictionary & Rule
		-> To extract information it is necessary to select the Part-Of-Speech(POS) model from Stanford

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File Information - Folder: ...DefaultFolder\1.files
================
input (Folder: ...DefaultFolder\1.files\1.input-file)
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	-> It should contains the HTML, PDF, TXT and XML files
	-> ATENTION: The name of the file must be a number, e.g., 1.html, 1.pdf. 1.txt, 1.xml,...

output (Folder: ...DefaultFolder\1.files\2.exported-file)
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	-> Folder will contain the exported TXT or XML files from the database

==================================
Detailed Information about Phase 1 - Folder: ...DefaultFolder\2.classification
==================================
input (Folder: ...DefaultFolder\2.classification\1.model)
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	-> Model Classification (model file)
	-> Attribute Structure (ARFF file) which contains the structure of the attribute. It's necessary to build new unseen ARFF file

output (Folder: ...DefaultFolder\2.classification\2.unlabeled)
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	-> It contains the ARFF file with the unseen instances

output (Folder: ...DefaultFolder\2.classification\3.labeled)
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	-> It contains the ARFF file with the classified instances

output (Folder: ...DefaultFolder\2.classification\4.log)
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	-> It contains the LOG file with the classified instances
	
==================================
Detailed Information about Phase 2 - Folder: ...DefaultFolder\3.extraction
==================================
input (Folder: ...DefaultFolder\3.extraction\1.tagger\stanford-models)
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	-> ...1.tagger\stanford-models
		-> POS Model Classification (tagger file) from Stanford
	-> ...1.tagger
		-> files stopword.txt & stoplist.txt used to remove falses postives

input (Folder: ...DefaultFolder\3.extraction\2.log.dictionary)
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	-> It contains the LOG file with term related to the sentene identified by the dictionary

input (Folder: ...DefaultFolder\3.extraction\3.log.rule)
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	-> It contains the LOG file with extracted information about technique 1 (Verb-Expression & POS) and technique 2 (POS)

========================
Elapsed Time Information 
========================
output (Folder: ...DefaultFolder\)
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		-> "dateTime".elapsedTime.log
			+ it contains information about the elapsed time of each selected algorithm

=====================================
Some informations about this version:
=====================================
	-> Used Weka API, version 3.7.0, to create the sentence model. Available at: http://www.cs.waikato.ac.nz/ml/weka/
	-> Used POS model from Stanford to classify the word according to the Part-Of-Speech. Available at: http://nlp.stanford.edu/software/tagger.shtml
	-> Properties files:
		+ "DefaultFolder.properties" file contains the default folder
		+ "connection.properties" file contains the database configuration
		+ "model.properties" file contains the sentence classification and the POS model 
	-> Type the following command  to run the project from the command line: 
		+ java -jar "SCA-Extractor.jar" 
	-> Type the following command to increase manually the Java heap space
		+ java -Xms300m -Xmx600m -jar "SCA-Extractor.jar" 		
		+ Xms and -Xmx are, respectively, the mininum and maximum memory
		+ this command will avoid the Exception java.lang.OutOfMemoryError		
	-> The recent version may be downloaded from http://gbd.dc.ufscar.br/~pablofmatos/files/SCA-Extractor.rar