Package: regex4dummies
Simple pattern finding in strings and natural language processing. Checkout regex4dummies’ website athttps://darkmattervale.github.io/regex4dummies/FeaturesAutomatic pattern detection ( semantic and literal )Multiple parsers ( implementations of nltk, pattern, and nlpnet )Keyword searching to find specific phrases within textTopic analysis & Important information extractionTokenizer and sentence dependency identifierPhrase extraction ( noun, verb, prepositional )String comparisonRoadmapSome 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 stringsAdditional parsersIf you have feature requests, feel free to add an issue to the Github issue tracker. All contributions and requests are appreciated!Usageregex4dummies is very easy to use. Simply import the library, get some strings, and compare them!fromregex4dummiesimportregex4dummiesfromregex4dummiesimportToolkit# Creating stringsstrings=["This is the first test string.","This is the second test string."]regex=regex4dummies()# Identifying literal patterns in stringsprintregex.compare_strings(parser='default',pattern_detection="literal",text=strings)# Identifying semantic patterns in strings using the nltk parserprintregex.compare_strings(parser='nltk',pattern_detection="semantic",text=strings)Above is regex4dummies in its simplest form. It allows for additional features as well, including:# Display the version of regex4dummies you are usingprintregex.__version__# To use the other parsers, replace the above line of code with either of the following:# print regex.compare_strings( parser='pattern', pattern_detection="semantic", text=strings )# print regex.compare_strings( parser='nlpnet', pattern_detection="semantic", text=strings )# To call all of the parsers, replace the above line of code with the following:# print regex.compare_strings( parser='', pattern_detection="semantic", text=strings )# To get the topics of the strings, call the get_topics functionprintregex.get_topics(text=strings)# Printing pattern informationpattern_information=regex.get_sentence_information()forobjectsinpattern_information:print"[ Pattern ]             : "+objects.patternprint"[ Subject ]             : "+objects.subjectprint"[ Verb ]                : "+objects.verbprint"[ 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""A newly released set of features include a tokenizer and dependency finder function. To use them, simply give a string as the first parameter, and the name of the parser you would like to use as the second for both functions.# Testing the toolkit functionstool_tester=Toolkit()# Testing the tokenizer functionsprinttool_tester.tokenize(text="This is a test string.",parser="")# Testing the dependency functionsprinttool_tester.find_dependencies(text="This is a test string.",parser="pattern")Other features included are demonstrated below.# Testing the information extraction functionsregex.extract_important_information(text=["This is a test string."])# Testing the ability to extract phrasesprint"Noun Phrases: "+str(tool_tester.extract_noun_phrases(text="This is a test string."))print"Verb Phrases(Pattern): "+str(tool_tester.extract_verb_phrases(text="This is a test string.",parser="pattern"))print"Verb Phrases(Nlpnet): "+str(tool_tester.extract_verb_phrases(text="This is a test string.",parser="nlpnet"))print"Prepositional Phrases: "+str(tool_tester.extract_prepositional_phrases(text="This is a test string in the house."))print"String comparison: "+str(tool_tester.compare_strings(String1="This is a test string.",String2="This is a test string."))InstallationTo install this library, use pip.$ pip install regex4dummiesIn 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:Download the nlpnet_dependency file on the most recent release found in Github ( please not, when uncompressed, this file is over 350 MB large ).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 Notesv1.4.6: Code refactoring, Download system reworkBrought code up to PEP8 standardsRedid download system. No more non-functional GUI; it uses automatic installation. In addition, the over size of the dependencies has decreased to ~700 MB from ~1.5 GBContributingContributors 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!LicensePlease see LICENSE.txt for information about the MIT licenseCitationsnlpnet: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]