Practical NLP with Lisp

Vsevolod DyomkinGrammarly

Topics

* Overview of NLP practice* Getting Data* Using Lisp: pros & cons* A couple of examples

A bit about Grammarly

(c) xkcd

An example of what we deal with

NLP practiceR - research work:set a goal →devise an algorithm →train the algorithm →test its accuracy

NLP practiceR - research work:set a goal →devise an algorithm →train the algorithm →test its accuracy

D - development work:implement the algorithm as an APIAPI with sufficient performanceperformance 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

A Note on Terminology

FN and FP instead of precision (P), recall (R)

FN = 1-RFP = 1-P or ???f1 = P*R/(P+R) =(1-FN-FP+FN*FP)/(2-(FN+FP))

Research contd.

2. Devise an algorithm3. Train & improve the algorithm

Research contd.

2. 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 (not whole) 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. (And data is key)

Theoretical maxima

Theoretical maxima are rarely achievable. Why?

* Because you need their data. (And data is key)

* 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

Where to get data?

Well-known sources:* Penn Tree Bank* Wordnet* Web1T Google N-gram Corpus* Linguistic Data Consortium (http://www.ldc.upenn.edu/)

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

Obscure corporaAcademic resources:* Stanford* CoNLL* Oxford (http://www.ota.ox.ac.uk/)* CMU, MIT,...* LingPipe, OpenNLP, NLTK,...

Human-powered?

http://goo.gl/hs4qB

Beyond corpora?

* Bootstrapping* Seeding

And remember...

“Data is ten times more powerful than algorithms.”

-- Peter Norvig, “The Unreasonable Effectiveness of Data.”http://youtu.be/yvDCzhbjYWs

Using Lisp for NLP

(c) xkcd

Why Lisp?Lisp is a carefully crafted tool for:

* Engineers* Practical researchers* Artists* Entrepreneurs

Some examples* Piano.aero* ITA Software* Secure Outcomes* Impromptu

* Land of Lisphttp://youtu.be/HM1Zb3xmvMc

Research requirements

* Interactivity* Mathematical basis* Expressiveness* Agility Malleability* Advanced tools

Specific NLP requirements

* Good support for statistics & number-crunching (matrices) Statistical AI–

* Good support for working with trees & symbols – Symbolic AI

Production requirements

* Scalability* Maintainability* Integrability* ...

...eventually

* Speed

...eventually

* Speed* Speed

...eventually

* Speed* Speed* Speed

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

Lisp drawbacksThere's no OpenNLP or SciPy & generally there's fewer libraries.

Lisp drawbacksThere'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

But #2Porter 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

More drawbacks

Lisp is a fringe language

Not special language(like R, J or Octave)

Example #1

API interaction

Example #2

Lisp FTW

* truly interactive environment* very flexible => DSLs* native tree support* fast and solid

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

Thanks!

Vsevolod Dyomkin@vseloved