Aspects of NLP Practice
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Transcript of Aspects of NLP Practice
Practical Aspectsof NLP Work
Vsevolod DyomkinGrammarly
TAAC'2012, Kyiv, Ukraine
Topics
* Practical vs Theoretical NLP work* Working with Data for NLP* NLP Tools
A bit about Grammarly
(c) xkcd
An example of what we deal with
Research vs Development
“Trick for productionizing research: read current 3-5 pubs and note the stupid simple thing they all claim to beat, implement that.
--Jay Kreps https://twitter.com/jaykreps/
status/219977241839411200
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
Research1. 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
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
Research
1. Set a goal2. Devise an algorithm3. Train & improve the algorithm
Research
1. Set a goal2. Devise an algorithm3. Train & improve the algorithm
http://nlp-class.org
4. Test its performance
ML: one corpus, divided into training,development,test
4. Test its performance
ML: one corpus, divided into training,development,test
Often different corpora:—* for training some part of the algorithm* for testing the whole system
Theoretical maxima
Theoretical maxima are rarely achievable. Why?
Theoretical maxima
Theoretical maxima are rarely achievable. Why?
* because you need their data
Theoretical maxima
Theoretical maxima are rarely achievable. Why?
* because you need their data
* domains might differ
Pre/post-processingWhat ultimately matters is not crude performance, but...
Pre/post-processingWhat ultimately matters is not crude performance, but...
Acceptance to users (much harder to measure & depends on domain).
Pre/post-processingWhat 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.
Examples ofpre-processing
For spellcheck:
* some people tend to use words, separated by slashes, like: spell/grammar check
* handling of abbreviations
Data
“Data is the next Intel Inside.
--Tim O'Reilly, What is Web2.0 http://oreilly.com/web2/archive/what-is-web-
20.html?page=3
Categorization of Data
* Structured small—* Semi-structured medium—* Unstructured big—
Where to get data?Well-known sources:* Penn Tree Bank* Wordnet* BNC* Web1T Google N-gram Corpus* Linguistic Data Consortium (http://www.ldc.upenn.edu/)
More dataAlso well-known sources, but with a twist:
* Wikipedia & Wiktionary, DBPedia* OpenWeb Common Crawl* Public APIs of some services: Twitter, Wordnik
Academic resources
* Stanford* CoNLL* Oxford (http://www.ota.ox.ac.uk/)* CMU, MIT,...* LingPipe, OpenNLP, NLTK,...
Crowd-sourced data
Jonathan Zittrain, The Future of the Internet
http://goo.gl/hs4qB
And remember...
“Data is ten times more powerful than algorithms.
--Peter Norvig The Unreasonable Effectiveness of Data http://youtu.be/yvDCzhbjYWs
Tools
Levels of NLP tools
High-level: user services
Middle-level: NLP algorithms
Low-level: data-crunching
Choosing a language
Requirement types:* Research* NLP-specific* Production
Research requirements
* Interactivity* Mathematical basis* Expressiveness* Agility Malleability* Advanced tools
Specific NLP requirements
* Good support for statistics & number-crunching – Statistical AI
* Good support for working with trees & symbols – Symbolic AI
Production requirements
* Scalability* Maintainability* Integrability* ...
Choose Lisp
(c) xkcd
Lisp FTW* Truly interactive environment* Very flexible => DSLs* Native tree support* Fast and solid
- No OpenNLP/NLTK
Heterogeneous systems
“Java way” vs. “Unix way”
Create language-agnostic systems, that can easily communicate!
Take-aways* As they say, in theory research and practice are the same, but in practice...
* Data is key. There are 3 types of it. Collect it, build tools to work with it easily and efficiently
* Choose a good language for R&D: interactive & malleable, with as few barriers as possible
Thanks!
Vsevolod Dyomkin@vseloved