edu.umass.cs.mallet.base.fst

Class SimpleTagger

java.lang.Object

edu.umass.cs.mallet.base.fst.SimpleTagger

public class SimpleTagger
extends Object

This class's main method trains, tests, or runs a generic CRF-based sequence tagger. Training and test files consist of blocks of lines, one block for each instance, separated by blank lines. Each block of lines should have the first form specified for the input of SimpleTagger.SimpleTaggerSentence2FeatureVectorSequence. A variety of command line options control the operation of the main program, as described in the comments for main.

Version:

1.0

Author:

Fernando Pereira

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	SimpleTagger.SimpleTaggerSentence2FeatureVectorSequence Converts an external encoding of a sequence of elements with binary features to a FeatureVectorSequence.

Method Summary

Modifier and Type	Method and Description
static Sequence[]	apply(Transducer model, Sequence input, int k) Apply a transducer to an input sequence to produce the k highest-scoring output sequences.
static void	main(String[] args) Command-line wrapper to train, test, or run a generic CRF-based tagger.
static void	test(Transducer model, TransducerEvaluator eval, InstanceList testing) Test a transducer on the given test data, evaluating accuracy with the given evaluator
static CRF4	train(InstanceList training, InstanceList testing, TransducerEvaluator eval, int[] orders, String defaultLabel, String forbidden, String allowed, boolean connected, int iterations, double var, CRF4 crf) Create and train a CRF model from the given training data, optionally testing it on the given test data.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Method Detail

train

public static CRF4 train(InstanceList training,
                         InstanceList testing,
                         TransducerEvaluator eval,
                         int[] orders,
                         String defaultLabel,
                         String forbidden,
                         String allowed,
                         boolean connected,
                         int iterations,
                         double var,
                         CRF4 crf)

Create and train a CRF model from the given training data, optionally testing it on the given test data.

Parameters:

training - training data

testing - test data (possibly null)

eval - accuracy evaluator (possibly null)

orders - label Markov orders (main and backoff)

defaultLabel - default label

forbidden - regular expression specifying impossible label transitions current,next (null indicates no forbidden transitions)

allowed - regular expression specifying allowed label transitions (null indicates everything is allowed that is not forbidden)

connected - whether to include even transitions not occurring in the training data.

iterations - number of traning iterations

var - Gaussian prior variance

Returns:

the trained model

test

public static void test(Transducer model,
                        TransducerEvaluator eval,
                        InstanceList testing)

Test a transducer on the given test data, evaluating accuracy with the given evaluator

Parameters:

model - a Transducer

eval - accuracy evaluator

testing - test data

apply

public static Sequence[] apply(Transducer model,
                               Sequence input,
                               int k)

Apply a transducer to an input sequence to produce the k highest-scoring output sequences.

Parameters:

model - the Transducer

input - the input sequence

k - the number of answers to return

Returns:

array of the k highest-scoring output sequences

main

public static void main(String[] args)
                 throws Exception

Command-line wrapper to train, test, or run a generic CRF-based tagger.

Parameters:

args - the command line arguments. Options (shell and Java quoting should be added as needed):

--help boolean

Print this command line option usage information. Give true for longer documentation. Default is false.

--prefix-code Java-code

Java code you want run before any other interpreted code. Note that the text is interpreted without modification, so unlike some other Java code options, you need to include any necessary 'new's. Default is null.

--gaussian-variance positive-number

The Gaussian prior variance used for training. Default is 10.0.

--train boolean

Whether to train. Default is false.

--iterations positive-integer

Number of training iterations. Default is 500.

--test lab or seg=start-1.continue-1,...,start-n.continue-n

Test measuring labeling or segmentation (start-i, continue-i) accuracy. Default is no testing.

--training-proportion number-between-0-and-1

Fraction of data to use for training in a random split. Default is 0.5.

--model-file filename

The filename for reading (train/run) or saving (train) the model. Default is null.

--random-seed integer

The random seed for randomly selecting a proportion of the instance list for training Default is 0.

--orders comma-separated-integers

List of label Markov orders (main and backoff) Default is 1.

--forbidden regular-expression

If label-1,label-2 matches the expression, the corresponding transition is forbidden. Default is \\s (nothing forbidden).

--allowed regular-expression

If label-1,label-2 does not match the expression, the corresponding expression is forbidden. Default is .* (everything allowed).

--default-label string

Label for initial context and uninteresting tokens. Default is O.

--viterbi-output boolean

Print Viterbi periodically during training. Default is false.

--fully-connected boolean

Include all allowed transitions, even those not in training data. Default is true.

--n-best positive-integer

Number of answers to output when applying model. Default is 1.

--include-input boolean

Whether to include input features when printing decoding output. Default is false.

Remaining arguments:

• training-data-file if training
• training-and-test-data-file, if training and testing with random split
• training-data-file test-data-file if training and testing from separate files
• test-data-file if testing
• input-data-file if applying to new data (unlabeled)

Throws:

Exception - if an error occurs