PmatchContainer

• class PmatchContainer
  • __init__ (self)
  • __init__ (self, defs)
  • match (self, input, time_cutoff = 0)
  • get_profiling_info (self)
  • set_verbose (self, b)
  • set_extract_tags_mode (self, b)
  • set_profile (self, b)
  • tokenize (self, input)
  • get_tokenized_output (self, input, **kwargs)

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class PmatchContainer

A class for performing pattern matching.

Probably the easiest way to perform pattern matching is with functions hfst.compile_pmatch_expression and hfst.compile_pmatch_file

---

__init__ (self)

Initialize a PmatchContainer. Is this needed?

---

__init__ (self, defs)

Create a PmatchContainer based on definitions defs.

• defs: A tuple of transducers in HFST_OLW_TYPE defining how pmatch is done.

An example:

If we have a file named streets.txt that contains:

define CapWord UppercaseAlpha Alpha* ;
define StreetWordFr [{avenue} | {boulevard} | {rue}] ;
define DeFr [ [{de} | {du} | {des} | {de la}] Whitespace ] | [{d'} | {l'}] ;
define StreetFr StreetWordFr (Whitespace DeFr) CapWord+ ;
regex StreetFr EndTag(FrenchStreetName) ;

and which has been earlier compiled and stored in file streets.pmatch.hfst.ol:

defs = hfst.compile_pmatch_file('streets.txt')
ostr = hfst.HfstOutputStream(filename='streets.pmatch.hfst.ol', type=hfst.ImplementationType.HFST_OLW_TYPE)
for tr in defs:
    ostr.write(tr)
ostr.close()

we can read the pmatch definitions from file and perform string matching with:

istr = hfst.HfstInputStream('streets.pmatch.hfst.ol')
defs = []
while(not istr.is_eof()):
    defs.append(istr.read())
istr.close()
cont = hfst.PmatchContainer(defs)
assert cont.match("Je marche seul dans l'avenue des Ternes.") == "Je marche seul dans l'<FrenchStreetName>avenue des Ternes</FrenchStreetName>."

See also: hfst.compile_pmatch_file, hfst.compile_pmatch_expression

---

match (self, input, time_cutoff = 0)

Match input input.

---

get_profiling_info (self)

todo

---

set_verbose (self, b)

todo

---

set_extract_tags_mode (self, b)

todo

---

set_profile (self, b)

todo

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tokenize(self, input)

Tokenize input and return a list of tokens i.e. strings.

• input: The string to be tokenized.

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get_tokenized_output(self, input, **kwargs)

Tokenize input and get a string representation of the tokenization (essentially the same that command line tool hfst-tokenize would give).

• input: The input string to be tokenized.
• kwargs: Possible parameters are: output_format, max_weight_classes, dedupe, print_weights, print_all, time_cutoff, verbose, beam, tokenize_multichar.
• output_format: The format of output; possible values are 'tokenize', 'xerox', 'cg', 'finnpos', 'giellacg', 'conllu' and 'visl'; 'tokenize' being the default.
• max_weight_classes: Maximum number of best weight classes to output (where analyses with equal weight constitute a class), defaults to None i.e. no limit.
• dedupe: Whether duplicate analyses are removed, defaults to False.
• print_weights: Whether weights are printd, defaults to False.
• print_all: Whether nonmatching text is printed, defaults to False.
• time_cutoff: Maximum number of seconds used per input after limiting the search.
• verbose: Whether input is processed verbosely, defaults to True.
• beam: Beam within analyses must be to get printed.
• tokenize_multichar: Tokenize input into multicharacter symbols present in the transducer, defaults to false.