Practical NLP with Lisp
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* Overview of NLP practice * Getting Data * Using Lisp: pros & cons
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Practical NLP with Lisp
- 1. Practical NLP with Lisp Vsevolod Dyomkin Grammarly
- 2. Topics * Overview of NLP practice * Getting Data * Using Lisp: pros & cons * A couple of examples
- 3. A bit about Grammarly (c) xkcd
- 4. An example of what we deal with
- 5. NLP practice R - research work: set a goal → devise an algorithm → train the algorithm → test its accuracy
- 6. NLP practice R - research work: set a goal → devise an algorithm → train the algorithm → test its accuracy D - development work: implement the algorithm as an API with sufficient performance and scaling characteristics
- 7. Research 1. Set a goal Business goal: * Develop best/good enough/better than Word/etc spellchecker * Develop a set of grammar rules, that will catch errors according to MLA Style * Develop a thesaurus, that will produce synonyms relevant to context
- 8. Translate it to measurable goal * On a test corpus of 10000 sentences with common errors achieve smaller number of FNs (and FPs), that other spellcheckers/Word spellchecker/etc * On a corpus of examples of sentences with each kind of error (and similar sentences without this kind of error) find all sentences with errors and do not find errors in correct sentences * On a test corpus of 1000 sentences suggest synonyms for all meaningful words that will be considered relevant by human linguists in 90% of the cases
- 9. A Note on Terminology FN and FP instead of precision (P), recall (R) FN = 1-R FP = 1-P or ??? f1 = P*R/(P+R) = (1-FN-FP+FN*FP)/(2-(FN+FP))
- 10. Research contd. 2. Devise an algorithm 3. Train & improve the algorithm
- 11. Research contd. 2. Devise an algorithm 3. Train & improve the algorithm http://nlp-class.org
- 12. 4. Test its performance ML: one corpus, divided into training,development,test
- 13. 4. Test its performance ML: one corpus, divided into training,development,test Often — different corpora: * for training some part (not whole) of the algorithm * for testing the whole system
- 14. Theoretical maxima Theoretical maxima are rarely achievable. Why?
- 15. Theoretical maxima Theoretical maxima are rarely achievable. Why? * Because you need their data. (And data is key)
- 16. Theoretical maxima Theoretical maxima are rarely achievable. Why? * Because you need their data. (And data is key) * Domains might differ
- 17. Pre/post-processing What ultimately matters is not crude performance, but...
- 18. Pre/post-processing What ultimately matters is not crude performance, but... Acceptance to users (much harder to measure & depends on domain).
- 19. Pre/post-processing What ultimately matters is not crude performance, but... Acceptance to users (much harder to measure & depends on domain). Real-world is messier, than any lab set-up.
- 20. Examples of pre-processing For spellcheck: * some people tend to use words, separated by slashes, like: spell/grammar check * handling of abbreviations
- 21. Where to get data? Well-known sources: * Penn Tree Bank * Wordnet * Web1T Google N-gram Corpus * Linguistic Data Consortium (http://www.ldc.upenn.edu/)
- 22. More data Also well-known sources, but with a twist: * Wikipedia & Wiktionary, DBPedia * OpenWeb Common Crawl (updated: 2010) * Public APIs of some services: Twitter, Wordnik
- 23. Obscure corpora Academic resources: * Stanford * CoNLL * Oxford (http://www.ota.ox.ac.uk/) * CMU, MIT,... * LingPipe, OpenNLP, NLTK,...
- 25. Beyond corpora? * Bootstrapping * Seeding
- 26. And remember... “Data is ten times more powerful than algorithms.” -- Peter Norvig, “The Unreasonable Effectiveness of Data.” http://youtu.be/yvDCzhbjYWs
- 27. Using Lisp for NLP (c) xkcd
- 28. Why Lisp? Lisp is a carefully crafted tool for: * Engineers * Practical researchers * Artists * Entrepreneurs
- 29. Some examples * Piano.aero * ITA Software * Secure Outcomes * Impromptu * Land of Lisp http://youtu.be/HM1Zb3xmvMc
- 30. Research requirements * Interactivity * Mathematical basis * Expressiveness * Agility Malleability * Advanced tools
- 31. Specific NLP requirements * Good support for statistics & number-crunching (matrices) – Statistical AI * Good support for working with trees & symbols – Symbolic AI
- 32. Production requirements * Scalability * Maintainability * Integrability * ...
- 35. ...eventually * Speed * Speed * Speed
- 36. Heterogeneous systems You have to split the system and communicate: “Java” way vs. “Unix” way * Sockets, Redis, ZeroMQ, etc for communication * JSON, SEXPs, etc for data
- 37. Lisp drawbacks There's no OpenNLP or SciPy & generally there's fewer libraries.
- 38. Lisp drawbacks There's no OpenNLP or SciPy & generally there's fewer libraries. But... * github: eslick/cl-langutils * github: mathematical-systems/clml * github: tpapp/lla * github: blindglobe/common-lisp-stat * … and http://quicklisp.org
- 39. But #2 Porter stemmer: http://tartarus.org/~martin/PorterStemmer & http://www.cliki.net/PorterStemmer or Soundex: http://www.cs.cmu.edu/afs/cs/project/ai- repository/ai/lang/lisp/code/0.html are irrelevant with good data
- 40. More drawbacks Lisp is a fringe language Not special language (like R, J or Octave)
- 42. Example #2
- 43. Lisp FTW * truly interactive environment * very flexible => DSLs * native tree support * fast and solid
- 44. Take-aways * Take nlp-class * Data is key, collect it, build tools to work with it easily and efficiently * A good language for R&D should be first of all interactive & malleable, with as few barriers as possible * ... it also helps if you don't need to port your code for production * Lisp is one of the good examples
- 45. Thanks! Vsevolod Dyomkin @vseloved