nlp project full cycle
Post on 25-Jan-2017
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A Bit about Me
* Lisp programmer* 5+ years of NLP work at Grammarly * Occasional lecturer
https://vseloved.github.io
Plan* Overview of NLP* NLP Data* Common NLP problems and approaches* Example NLP application: text language identification
What Is NLP?Transforming free-form text into structured data and back
Intersection of:* Computational Linguistics* CompSci & AI* ML, Stats, Information Theory
linguist [noun]1. A specialist in linguistics
linguistics [noun]1. The scientific study of language.
NLP DataTypes of text data:* structured* semi-structured* unstructured
“Data is ten times more
powerful than algorithms.”-- Peter NorvigThe Unreasonable Effectiveness of Data.http://youtu.be/yvDCzhbjYWs
Where to Get Data?* Linguistic Data Consortium http://www.ldc.upenn.edu/ * Common Crawl* Wikimedia* Wordnet* APIs: Twitter, Wordnik, ...* University sites & the academic community: Stanford, Oxford, CMU, ...
Create Your Own!* Linguists* Crowdsourcing* By-product
-- Johnatahn Zittrain http://goo.gl/hs4qB
Classic NLP Problems* Linguistically-motivated: segmentation, tagging, parsing
* Analytical: classification, sentiment analysis
* Transformation: translation, correction, generation
* Conversation:question answering, dialog
TokenizationExample:This is a test that isn't so simple: 1.23."This" "is" "a" "test" "that" "is" "n't" "so" "simple" ":" "1.23" "."
Issues:* Finland’s capital - Finland Finlands Finland’s* what’re, I’m, isn’t - what ’re, I ’m, is n’t* Hewlett-Packard or Hewlett Packard * San Francisco - one token or two?* m.p.h., PhD.
Regular ExpressionsSimplest regex: [^\s]+
More advanced regex:\w+|[!"#$%&'*+,\./:;<=>?@^`~…\(\) {}\[\|\]⟨⟩ ‒–—«»“”‘’-]―
Even more advanced regex:[+-]?[0-9](?:[0-9,\.]*[0-9])?|[\w@](?:[\w'’`@-][\w']|[\w'][\w@'’`-])*[\w']?|["#$%&*+,/:;<=>@^`~…\(\) {}\[\|\] «»“”‘’']⟨⟩ ‒–—―|[\.!?]+|-+
In fact, it works:https://github.com/lang-uk/ner-uk/blob/master/doc/tokenization.md
Rule-based Approach* easy to understand and reason about* can be arbitrarily precise* iterative, can be used to gather more data
Limitations:* recall problems* poor adaptability
researcher [noun]1. One who researches
research [noun]1. Diligent inquiry or examination to seek or revise facts, principles, theories, applications, etc.; laborious or continued search after truth
Statistical Approach
“Probability theoryis nothing butcommon sensereduced to calculation.”-- Pierre-Simon Laplace
Language Models
Question: what is the probability of a sequence of words/sentence?
Answer: Apply the chain rule
P(S) = P(w0) * P(w1|w0) * P(w2|w0 w1) * P(w3|w0 w1 w2) * …
where S = w0 w1 w2 …
NgramsApply Markov assumption: each word depends only on N previous words (in practice N=1..4 which results in bigrams-fivegrams, because we include the current word also).
If n=2: P(S) = P(w0) * P(w1|w0) * P(w2|w0 w1) * P(w3|w1 w2) * …
According to the chain rule:
P(w2|w0 w1) = P(w0 w1 w2) / P(w0 w1)
Spam FilteringA 2-class classification problem with a bias towards minimizing FPs.
Default approach: rule-based (SpamAssassin)
Problems:* scales poorly* hard to reach arbitrary precision* hard to rank the importance of complex features?
Bag-of-words Model* each word is a feature* each word is independent of others* position of the word in a sentence is irrelevant
Pros:* simple* fast* scalable
Limitations:* independence assumption doesn't hold
Bag-of-words Model* each word is a feature* each word is independent of others* position of the word in a sentence is irrelevant
Pros:* simple* fast* scalable
Limitations:* independence assumption doesn't hold
http://www.paulgraham.com/spam.html - A Plan for SpamInitial results: recall: 92%, precision: 98.84% Improved results: recall: 99.5%, precision: 99.97%
Naive Bayes Classifier
P(Y|X) = P(Y) * P(X|Y) / P(X)select Y = argmax P(Y|x) Naive step:
P(Y|x) = P(Y) * prod(P(x|Y)) for all x in X
(P(x) is marginalized out because it's the same for all Y)
Dependency Parsing
nsubj(ate-2, They-1)root(ROOT-0, ate-2)det(pizza-4, the-3)dobj(ate-2, pizza-4)prep(ate-2, with-5)pobj(with-5, anchovies-6)
https://honnibal.wordpress.com/2013/12/18/a-simple-fast-algorithm-for-natural-language-dependency-parsing/
Averaged Perceptron
def train(model, number_iter, examples): for i in range(number_iter): for features, true_tag in examples: guess = model.predict(features) if guess != true_tag: for f in features: model.weights[f][true_tag] += 1 model.weights[f][guess] -= 1 random.shuffle(examples)
ML-based ParsingThe parser starts with an empty stack, and a buffer index at 0, with no dependencies recorded. It chooses one of the valid actions, and applies it to the state. It continues choosing actions and applying them until the stack is empty and the buffer index is at the end of the input.
SHIFT = 0; RIGHT = 1; LEFT = 2 MOVES = [SHIFT, RIGHT, LEFT]
def parse(words, tags): n = len(words) deps = init_deps(n) idx = 1 stack = [0] while stack or idx < n: features = extract_features(words, tags, idx, n, stack, deps) scores = score(features) valid_moves = get_valid_moves(i, n, len(stack)) next_move = max(valid_moves, key=lambda move: scores[move]) idx = transition(next_move, idx, stack, parse) return tags, parse
The Hierarchy ofML Models
Linear:* (Averaged) Perceptron* Maximum Entropy / LogLinear / Logistic Regression; Conditional Random Field* SVM
Non-linear:* Decision Trees, Random Forests, Boosted Trees* Artificial Neural networks
SemanticsQuestion: how to model relationships between words?Answer: build a graph
WordnetFreebaseDBPedia
Word Similarity
Next question: now, how do we measure those relations?
* different Wordnet similarity measures
Word Similarity
Next question: now, how do we measure those relations?
* different Wordnet similarity measures
* PMI(x,y) = log(p(x,y) / p(x) * p(y))
Distributional Semantics
Distributional hypothesis:"You shall know a word bythe company it keeps"--John Rupert Firth
Word representations:* Explicit representation Number of nonzero dimensions: max:474234, min:3, mean:1595, median:415* Dense representation (word2vec, GloVe, …)* Hierarchical repr (Brown clusters)
Steps to Developan NLP System
* Translate real-world requirements into a measurable goal* Find a suitable level and representation* Find initial data for experiments* Find and utilize existing tools and frameworks where possible* Setup and perform a proper experiment (series of experiments)* Optimize the system for production
Going into Prod
* NLP tasks are usually CPU-intensive but stateless * General-purpose NLP frameworks are (mostly) not production-ready* Don't trust research results* Value pre- and post- processing* Gather user feedback
Text Language Identification
Not an unsolved problem:* https://github.com/CLD2Owners/cld2 - C++* https://github.com/saffsd/langid.py - Python* https://github.com/shuyo/language-detection/ - Java
To read:https://blog.twitter.com/2015/evaluating-language-identification-performancehttp://blog.mikemccandless.com/2011/10/accuracy-and-performance-of-googles.htmlhttp://lab.hypotheses.org/1083http://labs.translated.net/language-identifier/
YALI WILD
* All of them use weak models* Wanted to use Wiktionary — 150+ languages, always evolving* Wanted to do in Lisp
WILD Linguistics* Scripts vs languageshttp://www.omniglot.com/writing/langalph.htm
* Languages distributionhttps://en.wikipedia.org/wiki/Languages_used_on_the_Internet#Content_languages_for_websites
* Frequency word listshttps://invokeit.wordpress.com/frequency-word-lists/
* Word segmentation?
WILD DataWiktionary Wikipedia data:used abstracts, ~175 languages- download & store- process (SAX parsing)- setup learning & test data sets
10,778,404 unique words481,581 unique character trigrams
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