Post on 17-Dec-2015
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Ling 570: Day 8Classification, Mallet
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Roadmap
Open questions? Quick review of classification Feature templates
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Classification Problem Steps
Input processing: Split data into training/dev/test Convert data into a feature representation (aka
Attribute Value Matrix) Training Testing Evaluation
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Feature templates
Problem: predict the POS tag distribution of an unknown word
Input: “unfrobulate” Input: “turduckenly”
word w[-3..-1] w[-2..-1] w[-3..-1]==ate w[-3..-1]==nly w[-2,-1]=te w[-2,-1]=ly
unfrobulate ate te 1 0 1 0
turduckenly nly ly 0 1 0 1
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Feature templates
Problem: predict the POS tag distribution of an unknown word
Input: “unfrobulate” Input: “turduckenly”
Features might include:
word w[-3..-1] w[-2..-1] w[-3..-1]==ate w[-3..-1]==nly w[-2,-1]=te w[-2,-1]=ly
unfrobulate ate te 1 0 1 0
turduckenly nly ly 0 1 0 1
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Feature templates
Problem: predict the POS tag distribution of an unknown word
Input: “unfrobulate” Input: “turduckenly”
Features might include: Last three characters are “ate” Last two characters are “ly”
word w[-3..-1] w[-2..-1] w[-3..-1]==ate w[-3..-1]==nly w[-2,-1]=te w[-2,-1]=ly
unfrobulate ate te 1 0 1 0
turduckenly nly ly 0 1 0 1
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Feature templates
Problem: predict the POS tag distribution of an unknown word Input: “unfrobulate” Input: “turduckenly”
Features might include: Last three characters are “ate” Last two characters are “ly”
Feature templates generate features given an input Template : Last three characters == XXX.
word w[-3..-1] w[-2..-1] w[-3..-1]==ate w[-3..-1]==nly w[-2,-1]=te w[-2,-1]=ly
unfrobulate ate te 1 0 1 0
turduckenly nly ly 0 1 0 1
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Feature templates
Problem: predict the POS tag distribution of an unknown word Input: “unfrobulate” Input: “turduckenly”
Features might include: Last three characters are “ate” Last two characters are “ly”
Feature templates generate features given an input Template : Last three characters == XXX. Plug in XXX to get a binary valued feature. Templates generate many featuresword w[-3..-1] w[-2..-1] w[-3..-1]==ate w[-3..-1]==nly w[-2,-1]=te w[-2,-1]=ly
unfrobulate ate te 1 0 1 0
turduckenly nly ly 0 1 0 1
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Machine learning
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Classifiers
Wide variety Differ on several dimensions
Supervision
Learning Function
Input Features
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Supervision in Classifiers Supervised:
True label/class of each training instance is provided to the learner at training time
Naïve Bayes, MaxEnt, Decision Trees, Neural nets, etc
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Supervision in Classifiers Supervised:
True label/class of each training instance is provided to the learner at training time
Naïve Bayes, MaxEnt, Decision Trees, Neural nets, etc Unsupervised:
No true labels are provided for examples during training Clustering: k-means; Min-cut algorithms
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Supervision in Classifiers Supervised:
True label/class of each training instance is provided to the learner at training time
Naïve Bayes, MaxEnt, Decision Trees, Neural nets, etc Unsupervised:
No true labels are provided for examples during training Clustering: k-means; Min-cut algorithms
Semi-supervised: (bootstrapping) True labels are provided for only a subset of examples Co-training, semi-supervised SVM/CRF, etc
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Inductive Bias
What form of function is learned? Function that separates members of different classes
Linear separator Higher order functions Vornoi diagrams, etc
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Inductive Bias
What form of function is learned? Function that separates members of different classes
Linear separator Higher order functions Vornoi diagrams, etc
Graphically, decision boundary
+ + + - - -
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Machine Learning Functions
Problem: Can the representation effectively model the class to be learned?
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Machine Learning Functions
Problem: Can the representation effectively model the class to be learned?
Motivates selection of learning algorithm
++ + + + +
- - - - - - - - -
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Machine Learning Functions
Problem: Can the representation effectively model the class to be learned?
Motivates selection of learning algorithm
++ + + + +
- - - - - - - - -
For this function,Linear discriminant is GREAT!
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Machine Learning Functions
Problem: Can the representation effectively model the class to be learned?
Motivates selection of learning algorithm
++ + + + +
- - - - - - - - -
For this function,Linear discriminant is GREAT!Rectangular boundaries (e.g. ID trees)
TERRIBLE!
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Machine Learning Functions
Problem: Can the representation effectively model the class to be learned?
Motivates selection of learning algorithm
++ + + + +
- - - - - - - - -
For this function,Linear discriminant is GREAT!Rectangular boundaries (e.g. ID trees)
TERRIBLE!
Pick the right representation!
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Machine Learning Features
Inputs: E.g.words, acoustic measurements, parts-of-speech,
syntactic structures, semantic classes, ..
Vectors of features: E.g. word: letters
‘cat’: L1=c; L2 = a; L3 = t Parts of syntax trees?
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Machine Learning Features
Questions: Which features and values should be used? How should they relate to each other?
Issue 1: What values should they take? Binary features – don’t do anything! Real valued features *may* need to be normalized
Can force the values to have 0 mean and unit variance Compute the mean and variance on the training set for real valued
feature Replace original value with
Can also bin them or binarize them – often this works better Issue 2: Which ones are important?
Feature selection is sometimes important Current approach
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Machine Learning Toolkits
Many learners, many tools/implementations
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Machine Learning Toolkits
Many learners, many tools/implementations
Some broad tool sets weka
Java, lots of classifiers, pedagogically oriented
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Machine Learning Toolkits
Many learners, many tools/implementations
Some broad tool sets weka
Java, lots of classifiers, pedagogically oriented
mallet Java, classifiers, sequence learners More heavy duty
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Mallet: intro and data prep
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Mallet
Machine learning toolkit Developed at UMass Amherst by Andrew McCallum
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Mallet
Machine learning toolkit Developed at UMass Amherst by Andrew McCallum
Java implementation, open source
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Mallet
Machine learning toolkit Developed at UMass Amherst by Andrew McCallum
Java implementation, open source
Large collection of machine learning algorithms Targeted to language processing Naïve Bayes, MaxEnt, Decision Trees, Winnow, Boosting Also, clustering, topic models, sequence learners
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Mallet
Machine learning toolkit Developed at UMass Amherst by Andrew McCallum
Java implementation, open source
Large collection of machine learning algorithms Targeted to language processing Naïve Bayes, MaxEnt, Decision Trees, Winnow, Boosting Also, clustering, topic models, sequence learners
Widely used, but Research software: some bugs/gaps; odd documentation
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Installation
Installed on patas /NLP_TOOLS/tool_sets/mallet/latest/
Directories: bin/: script files src/: java source code class/: java classes lib/: jar files sample-data/: wikipedia docs for languages id, etc
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Environment Should be set up on patas
$PATH should include /NLP_TOOLS/tool_sets/mallet/latest/bin
$CLASSPATH should include /NLP_TOOLS/tool_sets/mallet/latest/lib/mallet-deps.jar;
/NLP_TOOLS/tool_sets/mallet/latest/lib/mallet.jar
Check: which text2vectors
/NLP_TOOLS/tool_sets/mallet/latest/bin
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Mallet Commands
Mallet command types: Data preparation Data/model inspection Training Classification
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Mallet Commands
Mallet command types: Data preparation Data/model inspection Training Classification
Command line scripts Shell scripts
Set up java environment Invoke java programs
--help lists command line parameters for scripts
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Mallet Data
Mallet data instances: Instance_id label f1 v1 f2 v2 …..
Stored in internal binary format: “vectors”
Binary format used by learners, decoders
Need to convert text files to binary format
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Data Preparation
Built-in data importers One class per directory, one instance per file
bin/mallet import-dir --input IF --output OF Label is directory name
(Also text2vectors)
One instance per line bin/mallet import-file --input IF --output OF
Line: instance label text ….. (Also csv2vectors)
Create binary representation of text feature counts
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Data Preparation
bin/mallet import-svmlight --input IF --output OF Allows import of user constructed feature value pairs
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Data Preparation
bin/mallet import-svmlight --input IF --output OF Allows import of user constructed feature value pairs Format:
label f1:v1 f2:v2 …..fn:vn Features can strings or indexes
(Also bin/svmlight2vectors)
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Data Preparation
bin/mallet import-svmlight --input IF --output OF Allows import of user constructed feature value pairs Format:
label f1:v1 f2:v2 …..fn:vn Features can strings or indexes
(Also bin/svmlight2vectors)
If building test data separately from original bin/mallet import-svmlight --input IF --output OF
--use-pipe-from previously_built.vectors
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Data Preparation
bin/mallet import-svmlight --input IF --output OF Allows import of user constructed feature value pairs Format:
label f1:v1 f2:v2 …..fn:vn Features can strings or indexes
(Also bin/svmlight2vectors)
If building test data separately from original bin/mallet import-svmlight --input IF --output OF
--use-pipe-from previously_built.vectors Ensures consistent feature representation
Note: can’t mix svmlight models with others
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Accessing Binary Formats vectors2info --input IF
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Accessing Binary Formats vectors2info --input IF
-- print-labels TRUE Prints list of category labels in data set
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Accessing Binary Formats vectors2info --input IF
-- print-labels TRUE Prints list of category labels in data set
-- print-matrix sic prints all features and values by string and number
Returns original text feature-value list Possibly out of order
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Accessing Binary Formats vectors2info --input IF
-- print-labels TRUE Prints list of category labels in data set
-- print-matrix sic prints all features and values by string and number
Returns original text feature-value list Possibly out of order
vectors2vectors --input IF --training-file TNF --testing-file TTF --training-portion pct
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Accessing Binary Formats vectors2info --input IF
-- print-labels TRUE Prints list of category labels in data set
-- print-matrix sic prints all features and values by string and number
Returns original text feature-value list Possibly out of order
vectors2vectors --input IF --training-file TNF --testing-file TTF --training-portion pct
Creates random training/test splits in some ratio
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Building & Accessing Models
bin/mallet train-classifier --trainer classifiertype - -training-portion 0.9 --output-classifier OF
Builds classifier model Can also store model, produce scores, confusion matrix, etc
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Building & Accessing Models
bin/mallet train-classifier --input vector_data_file --trainer classifiertype --training-portion 0.9 --output-classifier OF
Builds classifier model Can also store model, produce scores, confusion matrix, etc
--trainer: MaxEnt, DecisionTree, NaiveBayes, etc
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Building & Accessing Models
bin/mallet train-classifier --trainer classifiertype - -training-portion 0.9 --output-classifier OF
Builds classifier model Can also store model, produce scores, confusion matrix, etc
--trainer: MaxEnt, DecisionTree, NaiveBayes, etc --report: train:accuracy, test:f1:en
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Building & Accessing Models
bin/mallet train-classifier --trainer classifiertype - -training-portion 0.9 --output-classifier OF
Builds classifier model Can also store model, produce scores, confusion matrix, etc
--trainer: MaxEnt, DecisionTree, NaiveBayes, etc --report: train:accuracy, test:f1:en
Can also use pre-split training & testing files e.g. output of vectors2vectors --training-file, --testing-file
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Building & Accessing Models
bin/mallet train-classifier --trainer classifiertype - -training-portion 0.9 --output-classifier OF
Builds classifier model Can also store model, produce scores, confusion matrix, etc
--trainer: MaxEnt, DecisionTree, NaiveBayes, etc --report: train:accuracy, test:f1:en
Confusion Matrix, row=true, column=predicted accuracy=1.0 label 0 1 |total 0 de 1 . |1 1 en . 1 |1 Summary. train accuracy mean = 1.0 stddev = 0 stderr = 0 Summary. test accuracy mean = 1.0 stddev = 0 stderr = 0
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Accessing Classifiers
classifier2info --classifier maxent.model Prints out contents of model file
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Accessing Classifiers
classifier2info --classifier maxent.model Prints out contents of model file
FEATURES FOR CLASS en <default> -0.036953801963395115 book 0.004605219133228236 the 0.24270652500835088 i 0.004605219133228236
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Mallet: testing
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Testing Use new data to test a previously built classifier bin/mallet classify-svmlight --input testfile --output
outputfile --classifier maxent.model
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Testing Use new data to test a previously built classifier bin/mallet classify-svmlight --input testfile --output
outputfile --classifier maxent.model Also instance file, directories: classify-file, classify-dir
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Testing Use new data to test a previously built classifier bin/mallet classify-svmlight --input testfile --output
outputfile --classifier maxent.model Also instance file, directories: classify-file, classify-dir Prints class,score matrix
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Testing Use new data to test a previously built classifier bin/mallet classify-svmlight --input testfile --output
outputfile --classifier maxent.model Also instance file, directories: classify-file, classify-dir Prints class,score matrix
Inst_id class1 score1 class2 score2 array:0 en 0.995 de 0.0046 array:1 en 0.970 de 0.0294 array:2 en 0.064 de 0.935 array:3 en 0.094 de 0.905
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General Use bin/mallet import-svmlight --input svmltrain.vectors.txt
--output svmltrain.vectors Builds binary representation from feature:value pairs
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General Use bin/mallet import-svmlight --input svmltrain.vectors.txt
--output svmltrain.vectors Builds binary representation from feature:value pairs
bin/mallet train-classifier --input svmltrain.vectors –trainer MaxEnt --output-classifier svml.model
Trains MaxEnt classifier and stores model
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General Use bin/mallet import-svmlight --input svmltrain.vectors.txt
--output svmltrain.vectors Builds binary representation from feature:value pairs
bin/mallet train-classifier --input svmltrain.vectors –trainer MaxEnt --output-classifier svml.model
Trains MaxEnt classifier and stores model bin/mallet classify-svmlight --input svmltest.vectors.txt
--output - --classifier svml.model Tests on the new data
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Other Information
Website: Download and documentation (such as it is) http://mallet.cs.umass.edu
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Other Information
Website: Download and documentation (such as it is) http://mallet.cs.umass.edu
API tutorial: http://mallet.cs.umass.edu/mallet-tutorial.pdf
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Text Categorization Task:
Given a document, assign to one of finite set of classes What are the classes? What are the features?
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Text 1 Several hundred protesters, some wearing goggles and gas masks, marched
past authorities in a downtown street Sunday, hours after riot police forced Occupy Portland demonstrators out of a pair of weeks-old encampments in nearby parks.
Police moved in shortly before noon and drove protesters into the street after dozens remained in the camp in defiance city officials. Mayor Sam Adams had ordered that the camp shut down Saturday at midnight, citing unhealthy conditions and the encampment’s attraction of drug users and thieves.
Anti-Wall Street protesters and their supporters flooded a city park area in Portland early Sunday in defiance of an eviction order, and authorities elsewhere stepped up pressure against the demonstrators, arresting nearly two dozen. (Nov. 13)
More than 50 protesters were arrested in the police action, but officers did not use tear gas, rubber bullets or other so-called non-lethal weapons, police said.
Washington Post, online 11/13/2011
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Text 2
George Washington coach Mike Lonergan looked at the stat sheet, tried to muster a smile then clicked off the reasons why the Colonials lost to No. 24 California on Sunday night.
A piercing 21-0 run by the Golden Bears at the end of the first half was at the top of the list.
Not even a second straight 20-point effort from Tony Taylor was enough to dig George Washington out of the early hole, and the Colonials spent the rest of the night in a futile game of catch-up.
“I’ve never really been involved with a run quite like that,” Lonergan said after Cal’s 81-54 win over George Washington. “I tried calling a couple timeouts. It was very disappointing that we just never really got our composure back the rest of that half. To end it that way and not even score any points, that was basically the game right there.”
Washington Post, online 11/13/2011
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Test 3
‘Jersey Boys’ at the National Theatre By Jane Horwitz, Sunday, November 13, 5:29 PM “Jersey Boys” is irresistible, and the touring company now at the
National Theatre gets it almost entirely right. This Broadway hit (it has been running since fall 2005 and has
played Washington before as well) rises well above the so-called jukebox show genre. Subtitled “The Story of Frankie Valli & the Four Seasons,” the musical tells a tale that transcends show business gossip to become a close character study of four talented but very different blue-collar guys from New Jersey — who just happen to have sung some of the best close-harmony rock/pop tunes of the late 1950s, the 1960s and into the 1970s.
Washington Post, online 11/13/2011
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What categories?
What features?
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Example: CoreferenceQueen Elizabeth set about transforming her husband, King
George VI, into a viable monarch. Logue, a renowned speech therapist, was summoned to help the King overcome his speech impediment...
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Example: CoreferenceQueen Elizabeth set about transforming her husband, King
George VI, into a viable monarch. Logue, a renowned speech therapist, was summoned to help the King overcome his speech impediment...
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Example: CoreferenceQueen Elizabeth set about transforming her husband, King
George VI, into a viable monarch. Logue, a renowned speech therapist, was summoned to help the King overcome his speech impediment...
Can be viewed as a classification problem
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Example: CoreferenceQueen Elizabeth set about transforming her husband, King
George VI, into a viable monarch. Logue, a renowned speech therapist, was summoned to help the King overcome his speech impediment...
Can be viewed as a classification problem
What are the inputs?
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Example: CoreferenceQueen Elizabeth set about transforming her husband, King
George VI, into a viable monarch. Logue, a renowned speech therapist, was summoned to help the King overcome his speech impediment...
Can be viewed as a classification problem
What are the inputs?
What are the categories?
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Example: CoreferenceQueen Elizabeth set about transforming her husband, King
George VI, into a viable monarch. Logue, a renowned speech therapist, was summoned to help the King overcome his speech impediment...
Can be viewed as a classification problem
What are the inputs?
What are the categories?
What features would be useful?
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Example: NER Named Entity tagging:
John visited New York last Friday [person John] visited [location New York] [time last Friday]
As a classification problem John/PER-B visited/O New/LOC-B York/LOC-I last/TIME-B
Friday/TIME-I Input? Features? Classes?