gregdurrett / naacl13-morph

Code for the NAACL 2013 paper "Supervised Learning of Complete Morphological Paradigms"

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Wiktionary Morphology Code

This package contains code released for the paper:

Supervised Learning of Complete Morphological Paradigms Greg Durrett and John DeNero NAACL 2013

Usage

The main class is edu.berkeley.nlp.morph.Driver, which contains comments that document the usage and arguments.

The test/ directory includes a script, de-verb-small.sh, that trains and evaluates the model on some sample German verbs data (a subset of the accompanying Wiktionary Morphology dataset). It should report 73% whole template accuracy and 91.76% individual forms accuracy under "RESULTS:". The script also diffs the output against de-verb-small-output.txt; if the two match, then the system is working properly.

License

This software package is released under the New BSD License. Copyright (c) 2013, Greg Durrett All rights reserved.

Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:

  1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.

  2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.

  3. The names of its contributors may not be used to endorse or promote products derived from this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

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Code for the NAACL 2013 paper "Supervised Learning of Complete Morphological Paradigms"


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