README.md

portuguese-nlp

Nlp work on Brazil Portuguese newswire text

1. Preprocessing

Dataset

Number of news per year:

folder=~/brazil/data # /ai/home/acelebi/folca/data
for year in `ls $folder`;
  do
    count=`find $folder/$year -type f -name '*.html' | wc -l` ;
    printf "%s %s\n" $year $count ;
  done
  2004 7876
  2005 19368
  2006 18720
  2007 18579
  2008 19281
  2009 16337
  2010 24062
  2011 22372
  2012 25102
  2013 21714
  2014 20095
  2015 5526

Preprocess

Test clean:

python classification/main/clean.py --check_dir ~/brazil/data/2012/03
python classification/main/clean.py --raw_dir ~/brazil/data/2012/03 --parsed_dir /tmp/03/

Run parse:

for year in `ls $folder`;
  do 
    for month in `ls $folder/$year`
      do 
        mkdir -p ~/brazil/parsed_data/$year/$month; 
        python classification/main/clean.py --raw_dir ~/brazil/data/$year/$month --parsed_dir ~/brazil/parsed_data/$year/$month; 
      done; 
  done

Merge folders

  mydir=~/brazil/
  cd $mydir/parsed_data/
  mkdir ${mydir}/all_files_parsed
  find . -name '*.html' -size +512c -printf '%P\0' | pax -0rws ':/:_:g' ${mydir}/all_files_parsed
  ls ${mydir}/all_files_parsed/ | head
2005_01_01_0.html
2005_01_01_19.html
2005_01_01_1.html
2005_01_01_21.html

logs

cp -r /ai/home/acelebi/folca/data /tmp/brazil/
tar -zcpf /tmp/brazil/raw_data.tar.gz /tmp/brazil/data
# on TerraNova
scp guest7@balina.ku.edu.tr:/tmp/brazil/raw_data.tar.gz ~/brazil/
tar xzf ~/brazil/raw_data.tar.gz -C ~/brazil/

data_root=/home/cagil/brazil
year=2004
for month in `ls $data_root/data/$year`;         
  do            
    mkdir -p /tmp/parsed_data/$year/$month;            
    python classification/main/clean.py --raw_dir $data_root/data/$year/$month --parsed_dir /tmp/parsed_data/$year/$month;          
  done;

Next is 2. Crawling and Preparing Training Set

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