Description:

Dependency parsing is a lightweight syntactic formalism that relies on lexical relationships between words. Nonprojective dependency grammars may generate languages that are not context-free, offering a formalism that is arguably more adequate for some natural languages. Statistical parsers, learned from treebanks, have achieved the best performance in this task. While only local models (arc-factored) allow for exact inference, it has been shown that including non-local features and performing approximate inference can greatly increase performance. To learn the model, we implement a structured SVM with LP-relaxed inference.

This package contains a C++ implementation of an unlabeled dependency parser.

This package allows:

* learning the parser from a treebank,
* running the parser on new data,
* evaluating the results against a gold-standard.

To run this software, you need to have ILOG CPLEX installed in your system. ILOG is a commercial MILP solver. For more information regarding ILOG CPLEX, please go to http://www.ilog.com/products/cplex. You need also to have the Boost C++ libraries installed in your system.

Changes to previous version:

Initial Announcement on mloss.org.

BibTeX Entry: Download

Corresponding Paper BibTeX Entry: Download

Supported Operating Systems: Linux, Agnostic

Data Formats: Ascii

Tags: Dependency Parser, Large Margin Structured Classifier

Archive: download here

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