Word Sense Disambiguation (WSD) System
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- WordNet-SenseRelate System: http://sourceforge.net/projects/senserelate/
- SenseTools: http://www.d.umn.edu/~tpederse/sensetools.html A suite a tools that allow for easy creation of supervised word sense disambiguation experiments.
- WSD Shell: http://www.d.umn.edu/~tpederse/wsdshell.html This is a greatly improved version of the Duluth-Shell as used in the DuluthX Senseval-2 systems. It makes it easier to run large numbers of experiments, and provides many detailed reporting options.
- SyntaLex: http://www.d.umn.edu/~tpederse/syntalex.html This extends the Duluth Senseval-2 systems with part of speech and syntactic features. This system participated in Senseval-3 (2004).
- Duluth Senseval-3 Systems: http://www.d.umn.edu/~tpederse/senseval3.html Complete source code and documentation for the Duluth systems that participated in the Senseval-3 (2004) comparative exercise among word sense disambiguation systems. This includes supervised lexical sample systems based on the Duluth Senseval-2 systems, and a new unsupervised lexical sample system.
- DuluthX Senseval-2 Systems: http://www.d.umn.edu/~tpederse/senseval2.html Complete source code and documentation for the Duluth systems that participated in the lexical sample tasks of Senseval-2 (2001) comparative exercise among word sense disambiguation systems. These systems rely on lexical features like unigrams, bigrams, and co-occurrences.
- WSDGate: http://sourceforge.net/projects/wsdgate/ A complete word sense disambiguation system that integrates NSP and Weka into the Gate environment.
- CuiTools System: http://sourceforge.net/projects/cuitools/
- LingPipe System(?).
- See: Entity Mention Normalization System.
- Ted Pedersen - Free Software for Natural Language Processing from the NLP group at UMD
- (Joshi et al., 2006) ⇒ Mahesh Joshi, Serguei Pakhomov, Ted Pedersen, Richard Maclin, and Christopher Chute. (2006). “An End-to-end Supervised Target-Word Sense Disambiguation System.” In: Proceedings of AAAI-2006 (Intelligent System Demonstration).
- QUOTE: Word Sense Disambiguation (WSD) is the task of automatically deciding the sense of an ambiguous word based on its surrounding context. The correct sense is usually chosen from a predefined set of senses, known as the sense inventory. In target-word sense disambiguation the scope is limited to assigning meaning to occurrences of a few predefined target words in the given corpus of text. ... Most popular approaches to WSD use supervised machine learning methods to train a classifier using a set of labeled instances of the ambiguous word and create a statistical model. This model is then applied to unlabeled instances of the ambiguous word to decide their correct sense. In such approaches, the ability to run several experiments based on the choice of (i) features; and (ii) the classifier along with its parameters, is the key factor in determining the configuration that yields the best accuracy for the task under consideration. This is exactly what our system facilitates - an end-to-end interface for running several WSD experiments, with the choice of features using many existing and one new GATE (Cunningham et al. 2002) component and the choice of classifiers from WEKA (Witten & Frank 2005).