MTI ML - Machine Learning Package

Download

The main components of the MTI ML distribution are easily downloaded via the single MTI_ML.tar.gz link below. MTI ML also requires a third party package monq-1.1.1.jar which is available via the link below.

The monq-1.1.1.jar package is a third party open source development resource package that was originally available from berliOS. The monq-1.1.1.jar package is used to parse XML and provide server capabilities to MTI ML. We have preserved a static copy of the monq-1.1.1.jar file locally.

For best results, download and install the MTI ML distribution file MTI_ML.tar.gz and then download and install the monq-1.1.1.jar package in the new MTI_ML directory that was created by the MTI ML distribution file.

• MTI_ML.tar.gz (49 MB)
• monq-1.1.1.jar (221 KB) [originally from Third Party berliOS]

• mti_prod.jar: contains the core of the categorization algorithms
• utils.jar: contains tools to perform sorting

Installation

MTI ML has been compiled and verified to work with java 1.6. If you need to install java or update your current version, follow this link: http//www.java.com. Be sure that the path to the java program is in the PATH environment variable.

Move the downloaded files into a directory where you want to install MTI ML. When you uncompress and untar the MTI ML distribution file it will create a subdirectory call "MTI_ML" and that directory will be referred to as <parent_directory> throughout the rest of the instructions. So, for example, if you create a directory "Project" and install MTI ML in Project then the <parent_directory> should be set to <path to Project>/Project/MTI_ML or <path to Project>\Project\MTI_ML under Windows.

The various MTI ML jar files have to be added to the CLASSPATH environment variable or configured directly using the -cp parameter of the Java Virtual Machine (JVM).

Training and testing classifiers using MTI ML

• citations.train.xml.gz: training citations
• citations.test.xml.gz: test citations
• configuration.txt: configuration file used during training. This file provides the instructions to learn models to categorize citations related to the Humans, Male and Female MeSH headings.
• benchmark.test: file used as Gold Standard used to evaluate the performance of the algorithms on the test set

Using MTI ML on Female, Humans, and Male MeSH Headings

Move to the Parent Directory

[Drive]:

cd <parent_directory>

cd <parent_directory>

Setting the CLASSPATH environment variable

setenv CLASSPATH <parent_directory>/monq-1.1.1.jar:<parent_directory>/utils.jar:<parent_directory>/mti_prod.jar

export CLASSPATH=<parent_directory>/monq-1.1.1.jar:<parent_directory>/utils.jar:<parent_directory>/mti_prod.jar

CLASSPATH=<parent_directory>/monq-1.1.1.jar:<parent_directory>/utils.jar:<parent_directory>/mti_prod.jar
export CLASSPATH

Training

The file configuration.txt contains the details of the training. In this case, we are training models for the Humans, Male, and Female MeSH headings.

Details of the training are sent to the standard output. In the example, this is redirected to out.txt and can be used to follow the training progress and understand the generated model.

The training will take several minutes. Check the file out.log to ensure that there were no errors during training.

Testing

Annotating will take several minutes. Check the file annotation.log to ensure that there were no errors during annotating.

Evaluation

The evaluation file benchmark.txt contains results for all the MeSH headings. Each line shows the result for a single MeSH heading with fields separated by the pipe symbol. The first field is the MeSH heading name, then the number of positives in the test set, true positives, the false negatives, precision, recall, and F-measure.

The result for the MeSH headings Humans, Male and Female should be similar to these results:

Now that you have managed to train and evaluate classifiers based on the example data set, you can learn more about this tool from the document available here.

Publications and Resources

• MTI ML Package Publications
  
  
• Data Sets and Test Collections