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Keyword Extraction (1)

Perform keyword extraction using AlchemyAPI. See here for more information: http://www.alchemyapi.com/api/keyword/

Created: 2012-10-05 | Last updated: 2014-04-09

Credits: cneudecker

OpenCalais (1)

Perform various enrichments using the OpenCalais API. See here for more information: http://www.opencalais.com/about

Created: 2012-10-04

Credits: cneudecker

Concept Tagging (1)

Perform concept tagging using AlchemyAPI. See here for more information: http://www.alchemyapi.com/api/relation/

Created: 2012-10-04

Credits: cneudecker

Entity Extraction (1)

Perform entity extraction using AlchemyAPI. See here for more information: http://www.alchemyapi.com/api/entity/

Created: 2012-10-04

Credits: cneudecker

Sentiment Analysis (1)

Perform sentiment analysis using AlchemyAPI. See here for more information: http://www.alchemyapi.com/api/sentiment/

Created: 2012-10-04

Credits: cneudecker

Relation Extraction (1)

Perform relation extraction using AlchemyAPI. See here for more information: http://www.alchemyapi.com/api/relation/

Created: 2012-10-04 | Last updated: 2014-04-09

Credits: cneudecker

Matchbox Evaluation (1)

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Matchbox evaluation against ground truth. The evaluation process first creates the matchbox output and ground truth lists. It then counts each page tuple from the matchbox output that is in the ground truth as correctly identified tuple (true positive). Those that are not in the ground truth are counted as incorrectly identified tuples (false positives), and finally, those that are in the ground truth but not in the matchbox output are counted as missed tuples (false negatives). The precision...

Created: 2012-10-02 | Last updated: 2012-10-02

Credits: Sven

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Presentation - From SALAMI to Social Machines

Created: 2012-10-02 09:34:13 | Last updated: 2012-10-02 15:11:23

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Comments: 0 | Viewed: 17 times | Downloaded: 6 times

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Choosing the best k value for the k-NN cla... (1)

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The process determines the best value for the parameter k for the k-NN classification of the Breast Cancer Wisconsin (Diagnostic) data set available in the UCI Machine Learning Repository. The optimal k is computed by using 10-fold cross-validation. (To get better results each cross-validation is repeated 10 times and the averages of the runs are considered.) Finally, a k-NN classifier is built and evaluated on the entire data set using the optimal k. During the process the resulting average ...

Created: 2012-09-28 | Last updated: 2012-09-28