User:Pankajksharma/Patcher
Wiki page for GSoC work done by pankajksharma during GSoC 2014.
Application http://wiki.apertium.org/wiki/User:Pankajksharma/Application
For installation instructions, please visit: http://wiki.apertium.org/wiki/User:Pankajksharma/Patcher_Installation
More information will be added soon.
Contents
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)
