An implementation of the NBSVM classifier for Weka
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Updated
Feb 22, 2021 - Java
An implementation of the NBSVM classifier for Weka
Big data, map reduce, Hadoop, mallet, text classification
Automatic tool to detect Hate Speech in online user generated content
Mono Repository for GengoAI projects
Lab work for Natural Language Processing
Prototype based classification algorithm for multiple-instance data
Implementing a decision tree data type for text classification.
Morta is a proof-of-concept Java implementation of a span categorizer.
Text Classification of Legitimate and Rogue Online Privacy Policies: A manual analysis and an experimental procedure
Simple implementation of text classifier in Java with built in SVM, C4.5, kNN, and naive Bayesian classifiers. Support for common text preprocessors and for CVS format. You can plugin your own classifier, tokenizer, transformer, stopwords, synonyms, and TF-IDF formula etc. Supports automatic validation and confusion matrix.
The implementation aims to harness the power of Watson's text classification capabilities by leveraging Stanford CoreNLP for natural language processing and Lucene.Net for text indexing and search.
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This repo contains some test for a Text Classification based on models trained with the Weka Explorer. The code however does not work properly at the moment.
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Using the smile framework for text classification and additional preprocessing
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