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* Mavuno: A Hadoop-Based Text Mining Toolkit *
==============================================

Mavuno is a Hadoop-based open-source research toolkit that supports a variety of
large-scale distributed text mining operations, including:

* Pattern mining
* NLP Processing (POS Tagging, Chunking, Parsing, Named Entity Recognition)
* Distributional similarity
* Random walks over bi-partite graphs
* Paraphrase harvsting
* Class instance mining
* Semantic relation learning
* Information extraction

=======================
* System Requirements *
=======================

Mavuno requires the following software:
* Java 1.6 (or greater)
* Hadoop 0.20.2

======================
* Configuring Mavuno *
======================

The only configuration necessary is to ensure that the jar files in the
"lib" directory of the Mavuno distributions can be found in the Hadoop
classpath. The simplest way to achieve this is to copy the jars to the "lib"
directory of your Hadoop installation.

=================
* Documentation *
=================

Please see http://mavuno.isi.edu for the most recent documentation and
examples.

===========
* Contact *
===========

Direct all Mavuno-related questions, comments, bugs, and requests to:
[email protected] .

=========
* About *
=========

Mavuno was developed at the University of Southern California’s Information
Sciences Institute by Donald Metzler during 2010-2011. In October 2011, Mavuno
was released as an open source project for use by the broader research
community.

Mavuno is available under an Apache License, Version 2.0 (see LICENSE-2.0.txt
in the root directory of the distribution for more details).

We kindly ask that you use the following reference when citing Mavuno:

Metzler, D., and Hovy, E. "Mavuno: A Scalable and Effective Hadoop-Based
Paraphrase Acquisition System," to appear in the KDD Workshop on Large-scale
Data Mining: Theory and Applications(LDMTA 2011), 2011.

@inproceedings{Metzler:2011:MSE:2002945.2002948,
 author = {Metzler, Donald and Hovy, Eduard},
 title = {Mavuno: a scalable and effective Hadoop-based paraphrase acquisition system},
 booktitle = {Proceedings of the Third Workshop on Large Scale Data Mining: Theory and Applications},
 series = {LDMTA '11},
 year = {2011},
 isbn = {978-1-4503-0844-1},
 location = {San Diego, California},
 pages = {3:1--3:8},
 articleno = {3},
 numpages = {8},
 url = {http://doi.acm.org/10.1145/2002945.2002948},
 doi = {http://doi.acm.org/10.1145/2002945.2002948},
 acmid = {2002948},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {Hadoop, large-scale text mining, paraphrase acquisition},
}