• Project: DutchSemCor
• Project number: 380-70-011
• Funded by the Dutch National Science Foundation (NWO).
• website: https://www2.let.vu.nl/oz/cltl/dutchsemcor/index.html
• Start date: September, 2009
• End date: August, 2012
• Software: svm_wsd
• Version: 1.0
• Author: Ruben Izquierdo, VU University of Amsterdam
• Email: [email protected]

• dsc_wsd_tagger.py: main script for tagging an input plain text
• install.sh: script for installing libraries and dependencies
• COPYING-GPL.TXT
• LICENSESOFTWARE.TXT
• README.TXT

This work is licensed under a Creative Commons Attribution 3.0 Unported License: https://creativecommons.org/licenses/by/3.0/legalcode href="https://creativecommons.org/licenses/by/3.0/deed.en_US". See the file LICENSEDATA.TXT and COPYING-CC.TXT that should be in the top-directory of this distribution.

This program svm_wsd implements a machine learning Word Sense Disambiguation system based on Support Vector Machines. We use a bag-of-words model for representing the features. It has been implemented in python, so a valid installation of python is required, at least version 2.6. There are also some dependencies that will be automatically downloaded and installed by the script install.sh

• libsvm: library implementing a SVM engine (https://github.com/cjlin1/libsvm)
• treetagger: part-of-speech and lemmatizer (https://www.cis.uni-muenchen.de/~schmid/tools/TreeTagger) OR
• NafParserPy: parser of NAF files in python (https://github.com/cltl/NafParserPy)
• models_DSC: models trained on the DutchSemCor project for WSD

To install this program you have to follow 2 steps:

• 1. Download/clone this repository
• 2. Go to the folder and run the install.sh script, which will install libsvm, treetagger and the models for WSD. or
• 2. Go to the folder and run the install_naf.sh script to work with NAF files (not treetagger)

In summary:

$ git clone [email protected]:cltl/svm_wsd.git
$ cd svm_wsd
$ . install.sh OR . install_naf.sh

The input for the program has to be valid UTF-8 plain text. The script dsc_wsd_tagger.py will read the text from the standard input. So there are 2 easy ways to call the program:

For working with plain files and call treetagger for lemma and pos tagging:

$ echo 'Dit is een input text' | dsc_wsd_tagger.py
$ cat my_input_file.txt | dsc_wsd_tagger.py

The output will be an XML file with a structure similar to the one used by the SemCor corpus:

<text>
  <sent s_num="sentence number">
    <wf id="identifier" lemma="lemma" pos="part-of-speech' sense_confidence="value" sense_label="lexical-unit-label">token</wf>
    <wf...>
    ...
  </sent>
  <sent...
  ...
</text>

The attributes sense_confidence and sense_label are only present when the token is disambiguated.

For working with NAF files:

$ cat input.naf | dsc_wsd_tagger.py --naf -ref odwnSY> output.naf

The parameter ref represents what type of reference we want to have in the output:

• corLU: for cornetto lexical unit ids
• odwnLU: OpenDutchWordNet lexical unit ids
• odwnSY: OpenDutchWordNet synset ids.

The output is the NAF extended with the senses and confidences, represented as external references on the term--> externalReferences element. The ranking of all the senses with the returned confidence value according to SVM are included on the output.

• Ruben Izquierdo Bevia
• [email protected]
• https://rubenizquierdobevia.com/
• Vrije University of Amsterdam