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nlp-util

Random utilities for NLP. Many of them were designed for MT (Machine Translation) experiments, but they can still be used for general purposes.

Statistics and Analysis

Name Script Description
Word Count word-count.py Count (OOV/IV) words
Probability Histogram probability-histogram.py Generate a probability histogram
Vertical Statistics vertical-statistics.py Calculate statistics vertically for values (with fixed patterns)
Sequence Diff sequence-diff.py Compare sequences and display diffs

Corpus Preprocessing

Name Script Description
Bitext Identical Pairs bitext-identical-pairs.py Detect (and remove) identical pairs from bitext
Bitext Cleaning bitext-cleaning.py Clean bitext by heuristic rules
Corpus Name Script Description
ICWSM 2009 Spinn3r Blog Dataset Spinn3r-2009-extract.py Extract select (and clean) text
MSLT (Microsoft Speech Language Translation) MSLT-repack.sh, MSLT-extract.py Extract monolingual/parallel data
PPDB (Paraphrase Database) PPDB-extract.py Extract select paraphrases
XLIFF files XLIFF-extract.py Extract bitext from XLIFF files

Word Count

It can also be used for counting/getting OOV (out-of-vocabulary) or IV (in-vocabulary) words.

Sample output:

,	63751725
.	61725497
the	60873114
to	35743675
and	34360371
a	29438769
of	28104862
i	27116174
in	20234743
"	16950626
Usage:   word-count.py [-i INPUT] [-w WHITE_LIST] [-b BLACK_LIST] [-s]
Example: cat file | python word-count.py -w list -s > output
         cat file | python word-count.py -b vocabulary > oov
         cat file | python word-count.py -w vocabulary > iv
Optional arguments:
  -i INPUT, --input INPUT
                        input file(s) (glob patterns are supported)
  -w WHITE_LIST, --white-list WHITE_LIST
                        only count words in the write list
  -b BLACK_LIST, --black-list BLACK_LIST
                        ignore words in the black list
  -s, --statistics      print statistics (default: False)

Probability Histogram

Sample output:

-1.0	0.015589
-0.8	0.047416
-0.6	0.077869
-0.4	0.137002
-0.2	0.195826
0.0	0.102647
0.2	0.114418
0.4	0.134427
0.6	0.131316
0.8	0.043490
1.0

plot of probability-histogram

Usage:   probability-histogram.py [-i INPUT] [-c COLUMN] [-n] [-l LOWER] [-u UPPER] [-b BINS] [-p]
Example: cat file | python probability-histogram.py -c 1 -n -p
Optional arguments:
  -i INPUT, --input INPUT
                        input file(s) (glob patterns are supported)
  -c COLUMN, --column COLUMN
                        the index of column that contains values (default: 0)
  -n, --normalize       normalize scores to [-1,1] (default: False)
  -l LOWER, --lower LOWER
                        the lower range of bins
  -u UPPER, --upper UPPER
                        the upper range of bins
  -b BINS, --bins BINS  the number of bins (default: 10)
  -p, --plot            plot the histogram (default: False)

Vertical Statistics

Sample input:

BLEU = 33.99, 64.8/42.0/30.6/23.3 (BP=0.911, ratio=0.915, hyp_len=22925, ref_len=25061)
BLEU = 32.78, 65.5/40.9/28.2/20.2 (BP=0.933, ratio=0.935, hyp_len=55947, ref_len=59823)
BLEU = 37.29, 68.7/44.5/31.8/23.2 (BP=0.963, ratio=0.963, hyp_len=76162, ref_len=79064)

Sample output:

mean	BLEU = 34.69, 66.3/42.5/30.2/22.2 (BP=0.936, ratio=0.938, hyp_len=51678, ref_len=54649)
median	BLEU = 33.99, 65.5/42.0/30.6/23.2 (BP=0.933, ratio=0.935, hyp_len=55947, ref_len=59823)
Usage:   vertical-statistics.py [-i INPUT] [-l] [-c COLUMN]
                                [-m {mean,min,max,range,median,sum,std,var,sub} [{mean,...,sub} ...]]
Example: cat file1 file2 file3 | python vertical-statistics.py -l -m mean median > output
Optional arguments:
  -i INPUT, --input INPUT
                        input file(s) (glob patterns are supported)
  -m, --metrics {mean,min,max,range,median,sum,std,var,sub} [{mean,min,max,range,median,sum,std,var,sub} ...]
                        statistic metrics (default: ['mean'])
  -l, --label           print metrics labels (default: False)
  -c COLUMN, --column COLUMN
                        analyse a specified whitespace-split column (c-th) (default: None)

Sequence Diff

Sample output:

1 CONST-1	you can remove it .
....................................................................................................
1 SEQUE-B	you can take it off .
1 SEQUE-1	you can withdraw .
====================================================================================================
3 CONST-1	but , let 's face it , underachiever , dead @-@ end life , okay ?
....................................................................................................
3 SEQUE-B	let us be frank . he 's got a lousy job , he ain 't got no prospects .
           	                     ^  ----                 -----------           -
3 SEQUE-1	let us be frank . he has a lousy job , he no longer has any prospect .
           	                     ^^                     +++++++++++++++
====================================================================================================
Usage:   sequence-diff.py -f FILE [FILE ...] [-ft FILE_TAG [FILE_TAG ...]]
                          [-c CONST [CONST ...]] [-ct CONST_TAG [CONST_TAG ...]] [-d] [-m {char,token}] [-v]
Example: python sequence-diff.py -c source_file -f reference_file hypothesis_file
Optional arguments:
  -f FILE [FILE ...], --file FILE [FILE ...]
                        input files of sequences to be compared (the first file is the base to be compared with,
						such as reference translations) (default: None)
  -c CONST [CONST ...], --const CONST [CONST ...]
                        files of sequences not participating in the comparison,
						such as source sentences to be translated (default: [])
  -ft FILE_TAG [FILE_TAG ...], --file-tag FILE_TAG [FILE_TAG ...]
                        tags of input files (default: None)
  -ct CONST_TAG [CONST_TAG ...], --const-tag CONST_TAG [CONST_TAG ...]
                        tags of const files (default: None)
  -d, --condense        condense the comparison of multiple sequences without showing diffs (default: False)
  -m {char,token}, --mode {char,token}
                        compute diffs at character level or token level (default: char)
  -v, --verbose         print all sequences in the condense mode (default: False)

Bitext Identical Pairs

Sample output:

19056	inclusion=True
FILE-1	We' re on our way , way , way , we' re on our way
FILE-2	♪ We 're on our way , way , way ♪ ♪ We 're on our way , way , way , we 're on our way ... ♪
====================================================================================================
21584	similarity=0.68
FILE-1	We want to make a place we can learn to love , anywhere we can be proud of .
FILE-2	♪ We wanna make a place where we can learn to love ♪ ♪ Build a world that we can be proud of ♪
====================================================================================================
27541623 bitext pairs were read
770532 pairs (2.80%) were identical with inclusion and threshold=0.50
Usage:   bitext-identical-pairs.py [-f FILE [FILE ...]] [-o OUTPUT [OUTPUT ...]] [-i]
                                   [-t THRESHOLD] [-c] [-p] [-l] [-u] [-v]
Example: python bitext-identical-pairs.py -f file1 file2 -o output1 output2 -i -t 0.5 -p -l -v
Optional arguments:
  -f FILE [FILE ...], --file FILE [FILE ...]
                        input bitext file(s) to be compared (default: None)
  -o OUTPUT [OUTPUT ...], --output OUTPUT [OUTPUT ...]
                        output bitext file(s) without identical pairs (default: None)
  -i, --inclusion       treat inclusion as identity (default: False)
  -t THRESHOLD, --threshold THRESHOLD
                        similarity threshold to determine identity ([0,1]) (default: 0.9)
  -c, --character       calculate character-level similarity (default: False)
  -p, --punc-digit      exclude punctuations and digits from comparison (default: False)
  -l, --lowercase       compare lowercased sequences (default: False)
  -u, --capitalized     compare capitalized sequences (default: False)
  -v, --verbose         print identical pairs (default: False)

Bitext Cleaning

Sample output:

3       length-ratio=2.85 
FILE-1  Саvеndіѕh , mais la totalité s' élève à ... 2,343 livres et 16 cts .
FILE-2  2,343 pounds and 16 pence .
====================================================================================================
15      uppercase=True
FILE-1  ЅΑΝ FRΑΝСΙЅСΟ , 1973
FILE-2  SAN FRANCISCO , 1973
====================================================================================================
27541623 bitext pairs were read
2960667 pairs (10.75%) were filtered out
- 2944687 pairs (10.69%) were imbalanced with length-ratio >= 2.00
- 19386 pairs (0.07%) were uppercased (both source and target)
233423 pairs (0.85%) have been capitalized
Usage:   bitext-cleaning.py [-f FILE [FILE ...]] [-o OUTPUT [OUTPUT ...]] [-r RATIO] [-i] [-u] [-v]
Example: python bitext-cleaning.py -f file1 file2 -o output1 output2 -r 2.0 -u -v
Optional arguments:
  -f FILE [FILE ...], --file FILE [FILE ...]
                        input bitext file(s) (default: None)
  -o OUTPUT [OUTPUT ...], --output OUTPUT [OUTPUT ...]
                        output bitext file(s) (default: None)
  -r RATIO, --ratio RATIO
                        remove pairs which length ratios are no less than a threshold (default: None)
  -i, --incomplete      remove pairs if they contain incomplete sentences,
                        i.e. no .!?" at the end (default: False)
  -u, --uppercase       remove pairs if both source and target are uppercased,
                        otherwise capitalize uppercase strings (default: False)
  -v, --verbose         print identified pairs (default: False)

ICWSM 2009 Spinn3r Blog Dataset

Usage:    Spinn3r-2009-extract.py -f FILE [FILE ...] [-l LANGUAGES [LANGUAGES ...]]
                                  -e ELEMENTS [ELEMENTS ...] [-u] [-c]
Examples: python Spinn3r-2009-extract.py -f BLOGS-tiergroup-1.tar.gz -e title description -l en -u -c > output.en
Optional arguments:
  -f FILE [FILE ...], --file FILE [FILE ...]
                        Spinn3r tar.gz file(s)
  -l LANGUAGES [LANGUAGES ...], --languages LANGUAGES [LANGUAGES ...]
                        language(s) to be extracted (e.g. en)
  -e ELEMENTS [ELEMENTS ...], --elements ELEMENTS [ELEMENTS ...]
                        element(s) to be extracted (e.g. title, description)
  -u, --unescape        unescape text (e.g. "&"->"&") (default: False)
  -c, --clean           clean text (drop <*>/URLs, condense spaces) (default: False)

MSLT (Microsoft Speech Language Translation)

  1. Repack MSLT text (Python has an issue in handling original zip file).
bash MSLT-repack.sh /absolute/path/to/MSLT_Corpus.zip
  1. Extract parallel or monolingual data from MSLT_Corpus.tgz
Usage:    MSLT-extract.py -f FILE -s SOURCE [-t TARGET] [-c CATEGORY] [-o OUTPUT]
Examples: python MSLT-extract.py -f MSLT_Corpus.tgz -s fr -t en -c dev -o MSLT.fr-en
          python MSLT-extract.py -f MSLT_Corpus.tgz -s fr > MSLT.fr
Optional arguments:
  -f FILE, --file FILE  input repacked tgz file
  -s SOURCE, --source SOURCE
                        source language (e.g. fr)
  -t TARGET, --target TARGET
                        target language (e.g. en)
  -c CATEGORY, --category CATEGORY
                        dev or test? (default: dev)
  -o OUTPUT, --output OUTPUT
                        output file (used for parallel data)

PPDB (Paraphrase Database)

Usage:    PPDB-extract.py [-f FILE] [-a FEATURE] [-t THRESHOLD] [-e ENTAILMENT]
Examples: gzip -dc ppdb-2.0-s-lexical.gz | python PPDB-extract.py -e Equivalence > output
Optional arguments:
  -f FILE, --file FILE  unzipped input file(s) (glob patterns are supported)
  -a FEATURE, --feature FEATURE
                        the feature used for filtering
  -t THRESHOLD, --threshold THRESHOLD
                        the threshold used for filtering (feature value >= threshold are kept)
  -e ENTAILMENT, --entailment ENTAILMENT
                        the entailment type(s) used for filtering (regular expression)

XLIFF files

Usage:    XLIFF-extract.py [-h] -f FILE [-s {source,target,both}]
Examples: python XLIFF-extract.py -f RAPID_2019.de-en.xlf > output
Optional arguments:
  -f FILE, --file FILE  XLIFF file (default: None)
  -s {source,target,both,reverse}, --side {source,target,both,reverse}
                        side(s) of the bitext to be extracted (default: both)

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