regex4dummies 1.3.7

Features

• Automatic pattern detection ( semantic and literal )

• Multiple parsers ( implementations of nltk, pattern, and nlpnet )

• Keyword searching to find specific phrases

Roadmap

Some features I plan to implement in the future:

• Machine Learning. This will allow the parsers to learn multiple grammatical “styles” and be able to successfully parse a much wider selection of strings

• Additional parsers

• Continued improvements on the current parsers. This will be a long-term project, and additional details can be found on the main Github page

If you have feature requests, feel free to add an issue to the Github issue tracker. All contributions and requests are appreciated!

Usage

regex4dummies is very easy to use. Simply import the library, get some strings, and compare them!

from regex4dummies import regex4dummies

# Creating strings
strings = [ "This is the first test string.", "This is the second test string." ]

regex = regex4dummies()

# Identifying literal patterns in strings
print regex.compare_strings( 'default', True, strings )

# Identifying semantic patterns in strings using the nltk parser
print regex.compare_strings( 'nltk', False, strings )

Above is regex4dummies in its simplest form. It allows for additional features as well, including:

# Display the version of regex4dummies you are using
print regex.__version__

# To use the other parsers, replace the above line of code with either of the following:
# print regex.compare_strings( 'pattern', False, strings )
# print regex.compare_strings( 'nlpnet', False, strings )

# To call all of the parsers, replace the above line of code with the following:
# print regex.compare_strings( '', False, strings )

# To get the topics of the strings, call the get_pattern_topics function
print regex.get_pattern_topics()

# Printing pattern information
pattern_information = regex.get_sentence_information()
  for objects in pattern_information:
      print "[ Pattern ]             : " + objects.pattern
      print "[ Subject ]             : " + objects.subject
      print "[ Verb ]                : " + objects.verb
      print "[ Object ]              : " + objects.object[0]
      print "[ Prep Phrases ]        : " + str( objects.prepositional_phrases )
      print "[ Reliability Score ]   : " + str( objects.reliability_score )
      print "[ Applicability Score ] : " + str( objects.applicability_score )
      print ""

Installation

To install this library, use pip.

$ pip install regex4dummies

In addition to the library, wget is a required command-line command to use the nlpnet parser. If you do not have wget or cannot get it, follow the below directions to still get the functionality of the nlpnet parser.

Instructions to install the required dependency for nlpnet:

1. Download the nlpnet_dependency file on the most recent release found in Github ( please not, when uncompressed, this file is over 350 MB large ).

2. Place this directory into the same directory that nltk-data is located ( if you don’t have that installed, just run the library and go through the GUI downloader )

That’s it! The nlpnet parser should now be able to be used.

Patch Notes

( Latest ) v1.3.7: Bug fixes & minor updates

• First version of the accuracy tester released

• Another round of bug fixes. Some of the bugs found can cause aspects of regex4dummies to not be usable ( and crash your programs ), so if you encounter any bugs be sure to update to v1.3.7

• GUI update. The GUI should be compatible with the latest version of regex4dummies now ( it is not backwards compatible, however, so update to the latest version of the library to get full functionality ).

Contributing

Contributors are welcome and much needed! regex4dummies is still under heavy development, and needs all of the help it can get. If you have any feature ideas, feel free to create an issue on the github repository ( https://github.com/darkmattervale/regex4dummies/issues ) or fork the repository and create your addition.

Any help you can give is much appreciated. The more help we get, the better regex4dummies will perform. Thanks for contributing!

License

Please see LICENSE.txt for information about the MIT license

Citations

nlpnet:

• Fonseca, E. R. and Rosa, J.L.G. Mac-Morpho Revisited: Towards Robust Part-of-Speech Tagging. Proceedings of the 9th Brazilian Symposium in Information and Human Language Technology, 2013. p. 98-107 [PDF]