Check out our website at http://www.spark-n-spell.com

Note: All scripts were developed and tested using Python 2.7 and spark-1.5.0 and may not run as expected on other configurations.

(a) Run our Python port of SymSpell to correct individual words.

• download symspell_python.py to your local directory
• download the dictionary file big.txt to the same directory, from this github repository or one of the following additional sources:
  • https://github.com/dominedo/spark-n-spell/tree/master/testdata
  • http://norvig.com/ngrams/
  • s3n://spark-n-spell/big.txt
  • (or use your own dictionary file renamed as big.txt)
• at the prompt, run: python symspell_python.py
• type in the word to be corrected at the interactive prompt

(b) Run our Spark program to correct individual words.

• download word_correct_spark.py to your local directory (you should have Spark 1.5.0 installed, and you must be able to call spark-submit from that directory)
• if not already done, download the dictionary file big.txt from one of sources listed above
• at the prompt, run: spark-submit word_correct_spark.py -w "<word to correct>"
  • e.g. spark-submit word_correct_spark.py -w "cvhicken"

(c) Run our word-level Spark document checker.

Note this will be fairly slow as the current version internally generates all possible suggestions for each word in the test document. For a faster document checker, please run one of the context-level spellcheckers below.

• download word_level_doc_correct.py to your local directory (you should have Spark 1.5.0 installed, and you must be able to call spark-submit from that directory)
• if not already done, download the dictionary file big.txt from one of sources listed above
• download a document file to be tested into the working directory (some example test files of varying sizes can be found at https://github.com/dominedo/spark-n-spell/tree/master/testdata)
  • you will need to specify the name of this file when executing the python script as noted below using -c (otherwise, by default the script will look for a test document named test.txt in the working directory)
• at the prompt, run: spark-submit word_level_doc_correct.py -c "<.txt file to check>"
  • e.g. spark-submit word_level_doc_correct.py -c "test.txt"
  • optionally, you may add a -d file.txt argument to specify a different dictionary file
  • corrections are logged to log.txt in the local directory

(a) Run our Python implementation of context-level document checking.

• download contextSerial.py to your local directory
• if not already done, download the dictionary file big.txt from one of sources listed above
• download one of our sample test files from the testdata sub-folder, or prepare a .txt file of your own for checking
• at the prompt, run: python contextSerial.py
• use the custom parameter -d to override the default dictionary (big.txt) and/or the custom parameter -c to override the default document for checking (yelp100reviews.txt)
  • e.g. python contextSerial.py -d 'mycustomdictionary.txt' -c 'mycustomdocument.txt'

(b) Run our naive SPARK implementation of context-level checking.

• download contextSPARKnaive.py to your local directory
• if not already done, download the dictionary file big.txt from one of sources listed above
• download one of our sample test files from the testdata sub-folder, or prepare a .txt file of your own for checking
• at the prompt, run: spark-submit contextSPARKnaive.py
• use the custom parameter -d to override the default dictionary (big.txt) and/or the custom parameter -c to override the default document for checking (yelp100reviews.txt)
  • e.g. spark-submit contextSPARKnaive.py -d 'mycustomdictionary.txt' -c 'mycustomdocument.txt'

(c) Run our full SPARK implementation of context-level checking.

• download contextSPARKfull.py to your local directory
• if not already done, download the dictionary file big.txt from one of sources listed above
• download one of our sample test files from the testdata sub-folder, or prepare a .txt file of your own for checking
• at the prompt, run: spark-submit contextSPARKfull.py
• use the custom parameter -d to override the default dictionary (big.txt) and/or the custom parameter -c to override the default document for checking (yelp100reviews.txt)
  • e.g. spark-submit contextSPARKfull.py -d 'mycustomdictionary.txt' -c 'mycustomdocument.txt'

Consult our IPYTHON NOTEBOOKS for documentation on our coding and testing process.

• For word-level correction: word_level_documentation.ipynb

• For context-level correction: context_level_documentation.ipynb

  In order to view all related content and run the code, both files require the img , sample , and testdata sub-directories.

This repository also includes the following, for reference (see iPython Notebooks for details):

• other_versions folder:

  • serial_listsugg.py : word-level, word checker, serial, no early termination
  • serial_single.py : word-level, word checker, serial, same as symspell_python.py
  • serial_document.py : word-level, document checker, serial
  • spark_1.py : word-level, word checker, slow SPARK version
  • spark_2.py : word-level, word checker, faster SPARK version
  • spark_3.py : word-level, word checker, also fast SPARK version, same as word_correct_spark.py
  • spark_4.py : word-level, document checker, SPARK, same as word_level_doc_correct.py
  • contextSPARKapproximate.py : context-level, document-checker (warning: memory requirements grow exponentially with the size of the problem; only run on very small files e.g. test.txt)

• testdata folder: (all files also available at s3n://spark-n-spell/)

  • big.txt (6.5MB): used to create the dictionary and probability tables, where appropriate (source: http://norvig.com/ngrams/)
  • test.txt (106 bytes): variations of "this is a test"; used for early development and testing
  • yelp1review.txt (1KB): 1 Yelp restaurant review (183 words)
  • yelp10reviews.txt (8KB): 10 Yelp restaurant reviews (1,467 words)
  • yelp100reviews.txt (65KB): 100 Yelp restaurant reviews (12,029 words)
  • yelp250reviews.txt (173KB): 250 Yelp restaurant reviews (32,408 words)
  • yelp500reviews.txt (354KB): 500 Yelp restaurant reviews (66,602 words)
  • yelp1000reviews.txt (702KB): 1,000 Yelp restaurant reviews (131,340 words)