User:Pankajksharma/Patcher

Wiki page for GSoC work done by pankajksharma during GSoC 2014.

Application http://wiki.apertium.org/wiki/User:Pankajksharma/Application

More information will be added soon.

Apertium Python Patcher

Takes an Apertium language pair, a source-language sentence S, and a target-language sentence T, and outputs the set of all possible pairs of subsegments (s,t) such that s is a subsegment of S, t a subsegment of T and t is the Apertium translation of s or vice-versa (a subsegment is a sequence of whole words).

See http://wiki.apertium.org/wiki/User:Pankajksharma/Application#Proposal for more detail.

Algorithm

Algorithm for the on the fly patching is developed by Mikel L. Forcada and Pankaj K. Sharma.

On the fly patching (repair.py)

usage: repair.py [-h] [-d D] [--min-fms MIN_FMS] [--min-len MIN_LEN]

                [--max-len MAX_LEN]
                S T S1 LP

On the fly repairing of sentence.

positional arguments:

 S                  Second Sentence

 T                  First Sentence Translation

 S1                 Second Sentence

 LP                 Language Pair

optional arguments:

 -h, --help         show this help message and exit

 -d D               Specify the lanuguage-pair installation directory

 --min-fms MIN_FMS  Minimum value of fuzzy match score of S and S1.

 --min-len MIN_LEN  Minimum length of sub-segment allowed.

 --max-len MAX_LEN  Maximum length of sub-segment allowed.

fms.py

usage: fms.py [-h] S S1

Provides FMS of strings S and S1 using Wagner-Fischer algorithm.

positional arguments:

 S           First Sentence

 S1          Second Sentence

optional arguments:

 -h, --help  show this help message and exit

reg_test.py

Regression test for our patcher

usage: reg_test.py [-h] [-d D] [-v] [--mode MODE] [--min-fms MIN_FMS]

                  [--min-len MIN_LEN] [--max-len MAX_LEN]
                  out LP

positional arguments:

 out                Output file generated from preprocess.py (en-es.pairs and en-es.pairs.s are included) 

 LP                 Language Pair (sl-tl)

optional arguments:

 -h, --help         show this help message and exit

 -d D               Specify the lanuguage-pair installation directory

 -v                 Verbose Mode

 --mode MODE        Modes('all', 'cam', 'compare') default mode is all

 --min-fms MIN_FMS  Minimum value of fuzzy match score of S and S1.

 --min-len MIN_LEN  Minimum length of sub-string allowed.

 --max-len MAX_LEN  Maximum length of sub-string allowed.

Script understands following modes:

--all Includes all types of patched sentences

--cam Includes only those sentences which covers all mismatches

--compare Compares all reults for above two modes (verbose doesn't work in this mode)

Example usage: python reg_test.py en-es.pairs en-es --mode compare

preprocess.py

Preprocess the corpus for generating input for reg_test

usage: preprocess.py [-h] [-v] [--min-fms MIN_FMS] [--max-len MAX_LEN]

                    SLF TLF SLFT TLFT OUT

positional arguments:

 SLF                Source Language file for training

 TLF                Target Language file for training

 SLFT               Source Language file for testing

 TLFT               Target Language file for testing

 OUT                Output file for saving pairs

optional arguments:

 -h, --help         show this help message and exit

 --min-fms MIN_FMS  Minimum value of fuzzy match score of S and S1(default 0.8)

 --max-len MAX_LEN  Maximum length of sentences allowed (default 25)

 example: python preprocess.py ../ap/mtacat/en.en-es.train ../ap/mtacat/es.en-es.train ../ap/mtacat/en.en-es.testset ../ap/mtacat/es.en-es.test en-es.pairs -v

file_stats.py

Calculates and show a histogram of the distribution of FMS between pair of sentences present in corpus F.

usage: file_stats.py [-h] [--min-fms MIN_FMS] F

positional arguments:

 F                  Corpus path.

optional arguments:

 -h, --help         show this help message and exit

 --min-fms MIN_FMS  Minimum value of fuzzy match score of S and S1.

stats.py

usage: stats.py [-h] [-d D] [--min-fms MIN_FMS] [--min-len MIN_LEN]

               [--max-len MAX_LEN]
               D

Calulates FMS distribtution for all corpuses pressent in directory D.

positional arguments:

 D                  Corpus directory.

optional arguments:

 -h, --help         show this help message and exit

 -d D               Specify the lanuguage-pair installation directory

 --min-fms MIN_FMS  Minimum value of fuzzy match score of S and S1.

 --min-len MIN_LEN  Minimum length of sub-string allowed.

 --max-len MAX_LEN  Maximum length of sub-string allowed.

Set A generator

usage: A_generator.py [-h] [--min-fms MIN_FMS] [--min-len MIN_LEN]

                     [--max-len MAX_LEN]
                     S S1

Generates set A.

positional arguments:

 S                  First Sentence

 S1                 Second Sentence

optional arguments:

 -h, --help         show this help message and exit

 --min-fms MIN_FMS  Minimum value of fuzzy match score of S and S1.

 --min-len MIN_LEN  Minimum length of sub-string allowed.

 --max-len MAX_LEN  Maximum length of sub-string allowed.

Example: python A_generator.py "some string" "some another string" --min-fms=0.6 --min-len=1 --max-len=3

Expected Output:

("some string", "some another string")

("string", "another string")

Set D generator

Usage: python D_generator.py --helpusage: D_generator.py [-h] [-d D] [--min-fms MIN_FMS] [--min-len MIN_LEN] [--max-len MAX_LEN]

                     S T S1 LP

Generates set D.

positional arguments:

 S                  Second Sentence

 T                  First Sentence Translation

 S1                 Second Sentence

 LP                 Language Pair

optional arguments:

 -h, --help         show this help message and exit

 -d D               Specify the lanuguage-pair installation directory

 --min-fms MIN_FMS  Minimum value of fuzzy match score of S and S1.

 --min-len MIN_LEN  Minimum length of sub-string allowed.

 --max-len MAX_LEN  Maximum length of sub-string allowed.

Example: python D_generator.py "he changed his number recently" "Va canviar el seu número recentment" "he changed his address recently" en-ca

("Va canviar el seu", "Va canviar la seva adreça")

("Va canviar el seu número", "Va canviar el seu")

("Va canviar el seu número", "Va canviar la seva adreça")

("Va canviar el seu número recentment", "Va canviar la seva adreça recentment")

("El seu número", "El seu")

("El seu número", "La seva adreça")

("El seu número recentment", "La seva adreça recentment")

("Número recentment", "Recentment")

("Número recentment", "Adreça recentment")

pre_gsoc

For pre-SoC wok see [pre-soc/](https://github.com/pankajksharma/py-apertium/tree/master/pre_soc)