Working with sequences

Many data sets, such as text collections and genetic databases, consist of sequences of distinct values. MALLET includes implementations of widely used sequence algorithms including hidden Markov models (HMMs) and linear chain conditional random fields (CRFs). These algorithms support applications such as gene finding and named-entity recognition.

For a general introduction to CRFs, there are tutorials such as Sutton and McCallum (2006). A developer’s guide is available for sequence tagging in MALLET. The MALLET Javadoc API contains information for programmers interested in incorporating sequence tagging into their own work, in the cc.mallet.fst package. For semi-supervised sequence labeling, see this tutorial.

SimpleTagger

SimpleTagger is a command line interface to the MALLET Conditional Random Field (CRF) class. Here we present an extremely simple example showing the use of SimpleTagger to label a sequence of text. Your input file should be in the following format:

    Bill CAPITALIZED noun
    slept non-noun
    here LOWERCASE STOPWORD non-noun

That is, each line represents one token, and has the format:

feature1 feature2 ... featuren label

Then you can train a CRF using SimpleTagger like this (on one line), where train.txt is the name of the file with the lines shown above:

bin/mallet run cc.mallet.fst.SimpleTagger --train true --model-file nouncrf train.txt

The --train true option specifies that we are training, and --model-file nouncrf specifies where we would like the CRF written to. This produces a trained CRF in the file nouncrf.

If we have a file test.txt we would like labelled:

CAPITALIZED Al
        slept
        here

we can do this with the CRF in file nouncrf with:

bin/mallet run cc.mallet.fst.SimpleTagger --model-file nouncrf --include-input true test.txt

which produces the following output:

Number of predicates: 9
noun CAPITALIZED Al 
non-noun  slept 
non-noun  here 

If we remove --include-input true we will only print the output labels (noun, non-noun).

To use multi-threaded CRF training, specify the number of threads with --threads:

bin/mallet run cc.mallet.fst.SimpleTagger --train true --model-file nouncrf --threads 8 train.txt

A list of all the options available with SimpleTagger can be obtained by specifying the --help option:

bin/mallet run cc.mallet.fst.SimpleTagger --help