Examples of edu.ucla.sspace.coals.Coals

edu.ucla.sspace.coals.Coals

nbc.cmu.edu/~plaut/papers/pdf/RohdeGonnermanPlautSUB-CogSci.COALS.pdf">here

COALS first computes a term by term co-occurrance using a ramped 4-word window. Once all documents have been processed, the co-occurrence matrix will be re ordered such that only the {@code N} most frequent terms have theirsemantic vectors retained and only the {@code M} most frequent terms are usedas co-occurrence features. These values can be set by the {@value #MAX_WORDS_PROPERTY} and {@value MAX_DIMENSIONS_PROPERTY} properties,resepctively. After re ordering the semantic vectors and features, {@link CorrelationTransform} is used to rerank all co-occurrence scores. As part ofthis transform, all negative correlations are dropped and replaced with a 0. Finally, and optionally, the {@link SVD} is used to reduce the semanticspace. To set the number of retained dimensions via {@link SVD}, set the {@value REDUCE_DIMENSION_PROPERTY} property. @author Keith Stevens

        Transform transform = new CorrelationTransform();
        MatrixFactorization reducer = (argOptions.hasOption("reduce"))
            ? SVD.getFastestAvailableFactorization()
            : null;


        return new Coals(transform, reducer, 
                         argOptions.getIntOption("reducedDimension", 0),
                         argOptions.getIntOption("maxWords", 0),
                         argOptions.getIntOption("dimensions", 0));
    }

Examples of edu.ucla.sspace.coals.Coals

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