Software

TikZ-dependency

TikZ-dependency allows you to draw dependency graphs in LaTeX documents with little or no effort. It also comes with a lot of styling facilities, to let you personalize the look and feel of the graphs at your liking.

Main features:

  • Intuitive syntax to draw dependency graphs directly in LaTeX
  • High-level interface to define the look-and-feel of the graphs
  • Based on PGF/TikZ
  • Convenient macros to enrich the graphs with arbitrary TikZ elements
You can download it directly from CTAN, but the most up-to-date version is more likely to be found on SourceForge.
Please, do not hesitate to send me feedback, bug reports or feature requests!

Download FLinK

Version 1.1

Published: 02/1/2011

  • Fixed bug with dictionary merging (Thanks to Tim Miller)
  • Added support for Python 3.2.x

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Version 1.0

Published: 08/31/2011

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FLinK - A Framework for the Linearization of Kernel Functions

FLinK stands for “a framework for the linearization of kernel functions”.

The software has been developed as part of my PhD to support our research on the linearization of kernel functions, whose main results so far are published in the following papers authored by me with my co-advisor Alessandro Moschitti:

(My thesis contains more in-depth information, but I didn’t make it public yet because I am still fixing things here and there).

Although it could possibly be estended to support a large variety of kernel functions, at the moment the framework only supports the Syntactic Tree Kernel [Collins and Duffy, 2001] and the Partial Tree Kernel [Moschitti, 2006].

SVM-Light-TK by Alessandro Moschitti is used for learning and classification in Tree Kernel spaces.

For more information about the linearization process, please refer to the aforementioned papers. More details, as well as comprehensive documentation of the user interface and the programmer API can be found here.

FLinK is distributed under a double licensing scheme. For personal, teaching or research uses, the software is available under the GNU Lesser GPL (LGPL) v.3 license. The text of the license is available at http://www.fsf.org/licensing/licenses/lgpl.html. If you use this software for research, please consider referencing these papers [1],[2],[3] in your publications. Please note that research uses do NOT include those involving the development of technology to be employed for commercial or any other kind of revenue purposes. These include selling, releasing, or providing commercial services based on the software. For any other uses, the software is released under a commercial license. The terms of the license are defined on a per-request basis. You can contact me by email for more information.

Follow this link to download the latest version of the software.


References

  1. On Reverse Feature Engineering of Syntactic Tree Kernels, Pighin, Daniele, and Moschitti Alessandro , Conference on Natural Language Learning (CoNLL-2010), 08/2010, Uppsala, Sweden, (2010)
  2. Reverse Engineering of Tree Kernel Feature Spaces, Pighin, Daniele, and Moschitti Alessandro , EMNLP'09: Empirical Methods of Natural Language Processing, 08/2009, Singapore, (2009)
  3. Efficient Linearization of Tree Kernel Functions, Pighin, Daniele, and Moschitti Alessandro , CoNLL'09: Thirteenth Conference on Computational Natural Language Learning, 06/2009, Boulder, CO, USA, (2009)

Download ExRel

Version 0.9

Published: 07/22/2009

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ExRel - An Open Architecture for Relation Extraction and Semantic Role Labeling

Overview

ExRel (a not too original contraction for Relation Extraction) is a general framwork for relation extraction that we have been using for some years now as the main component of our architectures for Semantic Role Labeling (SRL) for the English and Arabic languages. 

Our approach to SRL is characterized by the combination of traditional linear features (e.g. Path, Governing Category or Phrase Type) with ad-hoc engineered structured features that we exploit by means of tree kernel functions.

Download structured features for SRL

Click here to download the requested file.

Mixed Features for Semantic Role Labeling

From this page you can download mixed structured (AST1m)/linear features data files that we used in our experiments on Semantic Role Labeling. The features are extracted from Charniak automatic parses as provided for the CoNLL 2005 shared task on SRL. The task and the extraction process are detailed in this paper.

MapNet: a FrameNet to WordNet Mapping

From this page you can download MapNet v.0.1 (28th May 2009)

Structured Features for Semantic Role Labeling

From this page you can download the structured features that were used in our experiments on Semantic Role Labeling. The features are extracted from Charniak automatic parses as provided for the CoNLL 2005 shared task on SRL. The task and the extraction process are detailed in this paper.

Software

FLinK

FLinK is a Framework for the Linearization of Kernel functions. More precisely, it allows to reverse-engineer models learned using tree kernel functions, and to isolate the most relevant structured features identified in the huge fragment space. It allows to perform very aggressive feature selection directly in the kernel space, and to transform a tree-kernel learning problem into a very efficient linear-space learning problem. Click here for more details.

ExRel

ExRel is a generic framework for the specification of relation extraction architectures, and a set of modules that implement a 2-Stage Semantic Role Labeling system. You can find more information here.

TikZ-dependency

Tikz-dependency is a LaTeX package to ease the drawing of dependency graph. Read more.

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