Changes between Version 15 and Version 16 of private/NlpInPracticeCourse/AutomaticCorrection
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- Dec 17, 2015, 9:56:36 PM (8 years ago)
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private/NlpInPracticeCourse/AutomaticCorrection
v15 v16 1 1 = Automatic language correction = 2 3 2 [[https://is.muni.cz/auth/predmet/fi/ia161|IA161]] [[en/AdvancedNlpCourse|Advanced NLP Course]], Course Guarantee: Aleš Horák 4 3 5 Prepared by: Ján Švec 4 Prepared by: Ján Švec 6 5 7 6 == State of the Art == 8 9 7 Language correction nowadays has many potential applications on large amount of informal and unedited text generated online, among other things: web forums, tweets, blogs, and email. Automatic language correction can consist of many areas including: spell checking, grammar checking and word completion. 10 8 … … 13 11 The lesson will also answer a question "How difficult is to develop a spell-checker?". And also describe a system that performs spell-checking and autocorrection. 14 12 15 In the end there will be a brief overview of various applications (computer software) for automatic language correction. 13 === References === 14 1. CHOUDHURY, Monojit, et al. "How Difficult is it to Develop a Perfect Spell-checker? A Cross-linguistic Analysis through Complex Network Approach" Graph-Based Algorithms for Natural Language Processing, pages 81–88, Rochester, 2007. [[http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=52A3B869596656C9DA285DCE83A0339F?doi=10.1.1.146.4390&rep=rep1&type=pdf|Source]] 15 1. WHITELAW, Casey, et al. "Using the Web for Language Independent Spellchecking and Autocorrection" Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, pages 890–899, Singapore, 2009. [[http://static.googleusercontent.com/external_content/untrusted_dlcp/research.google.com/en/us/pubs/archive/36180.pdf|Source]] 16 1. GUPTA, Neha, MATHUR, Pratistha. "Spell Checking Techniques in NLP: A Survey" International Journal of Advanced Research in Computer Science and Software Engineering, volume 2, issue 12, pages 217-221, 2012. [[http://www.ijarcsse.com/docs/papers/12_December2012/Volume_2_issue_12_December2012/V2I12-0164.pdf|Source]] 17 1. HLADEK, Daniel, STAS, Jan, JUHAR, Jozef. "Unsupervised Spelling Correction for the Slovak Text." Advances in Electrical and Electronic Engineering 11 (5), pages 392-397, 2013. [[http://advances.utc.sk/index.php/AEEE/article/view/898|Source]] 16 18 17 === References ===18 19 1. CHOUDHURY, Monojit, et al. "How Difficult is it to Develop a Perfect Spell-checker? A Cross-linguistic Analysis through Complex Network Approach" Graph-Based Algorithms for Natural Language Processing, pages 81–88, Rochester, 2007. [[http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=52A3B869596656C9DA285DCE83A0339F?doi=10.1.1.146.4390&rep=rep1&type=pdf|Source]]20 1. WHITELAW, Casey, et al. "Using the Web for Language Independent Spellchecking and Autocorrection" Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, pages 890–899, Singapore, 2009. [[http://static.googleusercontent.com/external_content/untrusted_dlcp/research.google.com/en/us/pubs/archive/36180.pdf|Source]]21 1. GUPTA, Neha, MATHUR, Pratistha. "Spell Checking Techniques in NLP: A Survey" International Journal of Advanced Research in Computer Science and Software Engineering, volume 2, issue 12, pages 217-221, 2012. [[http://www.ijarcsse.com/docs/papers/12_December2012/Volume_2_issue_12_December2012/V2I12-0164.pdf|Source]]22 1. HLADEK, Daniel, STAS, Jan, JUHAR, Jozef. "Unsupervised Spelling Correction for the Slovak Text." Advances in Electrical and Electronic Engineering 11 (5), pages 392-397, 2013. [[http://advances.utc.sk/index.php/AEEE/article/view/898|Source]]23 19 == Slides == 24 20 [http://nlp.fi.muni.cz/trac/research/raw-attachment/wiki/en/AdvancedNlpCourse/anlp-14-AutomaticCorrection.pdf] 25 21 26 22 == Practical Session == 23 In theoretical lesson we have become acquainted with various approaches how spelling correctors work. Now we will get to know how a simple spellchecker based on '''edit distance''' works. 27 24 28 The re will be a short overview of [[https://www.languagetool.org/|LanguageTool]] - Style and Grammar checker. Students can test the language correction algorithm and evaluate it on real data. After they become acquainted with how a spelling corrector works, we will write a simple spelling corrector in Python. The spelling corrector will be trained on a large text file compiled from [[https://www.gutenberg.org/|Project Gutenberg]]. The example will be based on Peter Norvig's [[http://norvig.com/spell-correct.html|Spelling Corrector]] in python. If the student finishes early the additional task is to enhance the spelling corrector's functionality.25 The example is based on Peter Norvig's [[http://norvig.com/spell-correct.html|Spelling Corrector]] in python. The spelling corrector will be trained with a large text file consisting of about a million words. 29 26 30 1. Download prepared script [[https://nlp.fi.muni.cz/trac/research/attachment/wiki/private/AdvancedNlpCourse/AutomaticCorrection/spell.py|spell.py]] and training data collection [[https://nlp.fi.muni.cz/trac/research/attachment/wiki/private/AdvancedNlpCourse/AutomaticCorrection/big.txt|big.txt]]. 31 1. Test the script {{{ python ./spell.py }}} in your working directory. 32 1. Open it in your favourite editor and we will walk through its functionality. 27 We will test this tool on prepared data. Your goal will be to enhance spellchecker's accuracy. If you finish early, there is a bonus question in the `task` section. 33 28 34 29 35 === Task === 30 1. Download prepared script [[https://nlp.fi.muni.cz/trac/research/attachment/wiki/private/AdvancedNlpCourse/AutomaticCorrection/spell.py|spell.py]] and training data collection [[https://nlp.fi.muni.cz/trac/research/attachment/wiki/private/AdvancedNlpCourse/AutomaticCorrection/big.txt|big.txt]]. 31 1. Test the script ` python ./spell.py ` in your working directory. 32 1. Open it in your favourite editor and we will walk through its functionality. 36 33 37 1. Create `<YOUR_FILE>`, a text file named ia161-UCO-14.txt where UCO is your university ID. 34 === Task === 35 1. Create `<YOUR_FILE>`, a text file named ia161-UCO-14.txt where UCO is your university ID. 38 36 39 2. Run `spell.py` with developement and final test sets (test1 and test2), write the results in `<YOUR_FILE>`. 37 2. Run `spell.py` with developement and final test sets (test1 and test2), write the results in `<YOUR_FILE>`. 40 38 41 3. Explain the given results in few words and write it in `<YOUR_FILE>`.39 3. Explain the given results in few words and write it in `<YOUR_FILE>`. 42 40 43 4. Modify the code of `spell.py` to increase accuraccy by 10 %. Write your new accuracy results to `<YOUR_FILE>`. 41 4. Modify the code of `spell.py` to increase accuraccy by 10 %. Write your new accuracy results to `<YOUR_FILE>`. 44 42 45 5. Run the script with `verbose=True` and examine given results. Try to suggest at least one adjustment how to enhance spellchecker's accuracy. Write your suggestions to `<YOUR_FILE>`. 46 47 -Bonus question- How could you make the implementation faster without changing the results? Write your suggestions to `<YOUR_FILE>`. 43 5. Run the script with `verbose=True` and examine given results. Try to suggest at least one adjustment how to enhance spellchecker's accuracy. Write your suggestions to `<YOUR_FILE>`. 48 44 45 -Bonus question- How could you make the implementation faster without changing the results? Write your suggestions to `<YOUR_FILE>`. 49 46 50 47 === Upload `<YOUR_FILE>` and edited `spell.py` === 51 52 48 Do not forget to upload your resulting files to the [https://is.muni.cz/auth/el/1433/podzim2015/IA161/ode/59241116/ homework vault (odevzdávárna)].
