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This module forms the main component of the ontology-based named entity recognition (NER). It can store arbitrary directed ontology graphs and supports multiple labels (ontological surface forms) per ontology graph node. It implements an easy to use class NamedEntityRecognition which can be used to (fuzzily) find the ontology instances in the text. This module has no dependencies.

The maven-duplicate-class-finder-plugin is a plugin that will search for classes with the same name, as well as resources with the same path, in the classpaths of a maven project. More specifically, it will check the compile, runtime, and test classpaths for * Classes with the same qualified name in the current project and all dependencies relevant for that classpath * Files that are not class files, with the same resource path (i.e. as if it would be accessed via the classloader) in the current project and all dependencies relevant for that (Note that at the moment, the plugin does not check if the files are actually the same or not, it only looks for the same file/class name.)

Processes an enum class annotated with @GenerateCucumberString, producing a new java source file which is also compiled. The generated class has a static String field, which is the concatenation of the enum values, with delimiter, suffix and prefix as specified in the annotation. These parameters (delimiter, suffix...) can be overridden. This field can be used in any annotation (but most useful in Cucumber annotations), as it's a compile time constant value.

Maven plugin for small application config support. Allows you to define configuration for your application at one place and generate config files and java class. Configuration can be instantiate by var args, file config or environment variables or combination of all. TODO: - boolean opts -- is/get -- option without value true - make def enum public - static instance to access from everywhere without pass an instance

Class implementing the predictive apriori algorithm for mining association rules. It searches with an increasing support threshold for the best 'n' rules concerning a support-based corrected confidence value. For more information see: Tobias Scheffer: Finding Association Rules That Trade Support Optimally against Confidence. In: 5th European Conference on Principles of Data Mining and Knowledge Discovery, 424-435, 2001.

This Content Negotiation Filter provides a content negotiation filter for Java EE servlet containers. The content negotiation algorithm is based on the Apache negotiation algorithm but lacks support for the encoding and character set. The variants can be configured through an XML file, negotiation.xml, in the WEB-INF/classes directory. This file can be overriden with the 'xmlInputFile' filter parameter. This XML file is parsed with the class 'com.mattunderscore.filter.contentnegotiation.variantsource.FilterVariantXMLSource'. This class can be overridden with the 'variantSource' filter parameter. The XML parser is implemented as a SAX parser. The SAX handler class can be overridden by the saxHandlerClass filter parameter. The 'variantSource' parameter can take any class that implements the 'com.mattunderscore.filter.contentnegotiation.variantsource.VariantSource'.

This will eventually be a port of Google's libphonenumber to Scala. For now, because I need this functionality quick, it simply wraps libphonenumber. It is NOT complete by any means. I've only added the functionality that I needed. Currently you get a PhoneNumber case class that assumes the country is USA when you don't pass the country in. While you can pass the country in, the only country defined is USA. If you're interested in more from this library, report an issue or send me a pull request. I want to complete it and slowly remove the dependency on Google's libphonenumber

Class for building and using a Complement class Naive Bayes classifier. For more information see: Jason D. Rennie, Lawrence Shih, Jaime Teevan, David R. Karger: Tackling the Poor Assumptions of Naive Bayes Text Classifiers. In: ICML, 616-623, 2003. P.S.: TF, IDF and length normalization transforms, as described in the paper, can be performed through weka.filters.unsupervised.StringToWordVector.

This package provides four search methods for attribute selection: ExhaustiveSearch, GeneticSearch, RandomSearch and RankSearch. See: David E. Goldberg (1989). Genetic algorithms in search, optimization and machine learning. Addison-Wesley. Mark Hall, Geoffrey Holmes (2003). Benchmarking attribute selection techniques for discrete class data mining. IEEE Transactions on Knowledge and Data Engineering. 15(6):1437-1447.

Binary-class and multi-class alternating decision trees. For more information see: Freund, Y., Mason, L.: The alternating decision tree learning algorithm. In: Proceeding of the Sixteenth International Conference on Machine Learning, Bled, Slovenia, 124-133, 1999. Geoffrey Holmes, Bernhard Pfahringer, Richard Kirkby, Eibe Frank, Mark Hall: Multiclass alternating decision trees. In: ECML, 161-172, 2001.