Materiais

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TaXEm - a tool for helping evaluate domain topics

The TaXEm (Taxonomia em XML da Embrapa) is a fast and efficient tool to organize, retrieve, browse and extract knowledge from textual documents. In order to organize specific domain information, TaXEm builds a taxonomy which can be (semi)/automatically evaluated. This evaluation can be carried out using objective measures or using a subjective analysis based on the domain specialist judgment.

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PRETEXT - Text preprocessing

PRETEXT is a computational tool implemented in Perl using the object oriented paradigm, which automatically performs most of the Text Mining pre-processing tasks in a collection of documents. The documents may be written in three different languages: Portuguese, Spanish and English. In addition, the tool includes facilities to reduce the dimensionality of any text pre-processed data set by using Zipf’s law and Luhn cut-offs.

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KNN-WEKA - A new k-nearest neighbor implementation for Weka

KNN-WEKA extends the current Weka k-nearest neighbor implementation by adding an example weighting function related to the distance from the current example to the query example. Moreover, KNN-Weka provides a distance function, known as the Heterogeneous Euclidean-VDM Metric (HVDM), which aims to better incorporate the information provided by nominal attributes.

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IESystem - Information Extraction System

The IESystem extracts metadata from scientific articles, even when they are provided from different sources or written in different languages. The process of metadata extraction is based on models which describe relative content positions or content indicators to be extracted. A set of functionalities for pre-processing and assistance to the user when constructing models are also available.

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Harpia - Hierarchical Classification Framework

Harpia is an open-source Java library for the development of machine learning algorithms which learn from hierarchically-labelled examples. It is named after the ''Harpia harpyja'' eagle. The implementation of this framework is based on the Weka Machine Learning API. Therefore, increasing the spectrum of available machine learning algorithms to be used as base classifiers in the context of "local" hierarchical classification algorithms. In addition, the evaluation module contains a variety of measures from three categories: example based, label based and level based. Furthermore, other measures such as the micro and macro average measures are also available.

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