Perceptronix Point Never
Perceptronix Point Never (PPN) is an implementation of a part of speech tagger using a hidden Markov model, the averaged perceptron classifier, and a greedy decoding scheme. The classifier features are based loosely on those used by Ratnaparkhi 1996 and Collins 2002. Following Collins, the same features, including orthographic features, are used regardless of word frequency.
PPN has been tested on CPython 3.4 and PyPy3 (2.3.1, corresponding to
Python 3.2); the latter is much, much faster. It requires three
jsonpickle from PyPI and my own
library, available from GitHub; see
requirements.txt for the versions
used for testing.
usage: python -m PPN [-h] [-v] [-V] (-t TRAIN | -r READ) (-u TAG | -w WRITE | -e EVALUATE) [-E EPOCHS] [-O ORDER] Perceptronix Point Never, by Kyle Gorman optional arguments: -h, --help show this help message and exit -v, --verbose enable verbose output -V, --really-verbose even more verbose output -t TRAIN, --train TRAIN training data -r READ, --read READ read in serialized model -u TAG, --tag TAG tag unlabeled data -w WRITE, --write WRITE write out serialized model -e EVALUATE, --evaluate EVALUATE evaluate on labeled data -E EPOCHS, --epochs EPOCHS # of epochs (default: 10) -O ORDER, --order ORDER Markov order (default: 2)
PPN-wsj.json.gz is a tagging model trained on the Wall St. Journal portion of the Penn Treebank.
For anything else, UTSL.
MIT License (BSD-like); see source.
If you use Perceptronix Point Never in your research, please consider linking readers to this repository and/or directing them to the following paper, which describes the tagger and reports an evaluation on the "standard split":
K. Gorman, S. Bedrick, G. Kiss, E. Morley,. R. Ingham, M. Mohammed, K. Papadakis, and J. van Santen. 2015. Automated morphological analysis of clincial languaeg samples. In CLPsych, 108-116.
What's with the name?
It is an homage to experimental musician Daniel Lopatin, who performs under the name Oneohtrix Point Never.
Contact Kyle Gorman.
M. Collins. 2002. Discriminative training methods for hidden Markov models: Theory and experiments with perceptron algorithms. In EMNLP, 1-8.
A. Ratnaparkhi. 1996. A maximum entropy model for part-of-speech tagging. In EMNLP, 133-142.