Minitagger is a tagger for words in sentences. Underlying the tagger is an implementation of a multi-class SVM (Fan et al., 2008). It makes independent predictions based on local context. Even though this method is completely unstructured (as opposed to CRFs), with the addition of lexical representations it performs as well as structured models on certain problems like POS tagging.

For experimental details, see: Simple Semi-Supervised POS Tagging (Stratos and Collins, 2015). You can obtain the word representations used in the experiments at: http://www.cs.columbia.edu/~stratos/research/wordrep.tar.gz.

Minitagger can:

1. Utilize bit string (Brown clusters) and real-valued (word embeddings) lexical features.

• These lexical features must include a representation for unknown words. By default, symbol "<?>" denotes this representation.

2. Train from partially or completely labeled data, of form (an empty line marks the end of a sentence):

     The
     dog
     saw	V
     the
     cat
   

3. Perform active learning using whatever features it's equipped with.

First, type make to compile the liblinear package.

• Try training a tagger with baseline features:

python3 minitagger.py example/example.train --model_path /tmp/example.model.baseline --train --feature_template baseline

• Try training a tagger with bit string features:

python3 minitagger.py example/example.train --model_path /tmp/example.model.bitstring --train --feature_template bitstring --bitstring_path example/example.bitstring

• Try training a tagger with embedding features:

python3 minitagger.py example/example.train --model_path /tmp/example.model.embedding --train --feature_template embedding --embedding_path example/example.embedding

Then try tagging test data:

python3 minitagger.py example/example.test --model_path [model] --prediction_path /tmp/example.test.prediction

• Try active learning with baseline features, seed size 1, and step size 1 (you can also provide a held-out dataset to monitor the improvement in a log file):

python3 minitagger.py example/example.train --train --feature_template baseline --active --active_output_path /tmp/active.baseline.seed1.step1 --active_seed_size 1 --active_step_size 1 --active_output_interval 1

Once you have actively selected examples, you can simply provide these partially labeled sentences as training data to train a model.