Extending Word2Vec for Performance and Semi-Supervised Learning

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MLLib Word2Vec is an unsupervised learning technique that can generate vectors of features that can then be clustered. But the weakness of unsupervised learning is that although it can say an apple is close to a banana, it can’t put the label of “fruit” on that group. We show how MLLib Word2Vec can be combined with the human-created data of YAGO2 (which is derived from the crowd-sourced Wikipedia metadata), along with the NLP metrics Levenshtein and Jaccard, to properly label categories. As an alternative to GraphX even though YAGO2 is a graph, we make use of Ankur Dave’s powerful IndexedRDD, which is slated for inclusion in Spark 1.3 or 1.4. IndexedRDD is also used in a second way: to further parallelize MLLib Word2Vec. The use case is labeling columns of unlabeled data uploaded to the Oracle Data Enrichment Cloud Service (ODECS) cloud app, which processes big data in the cloud.

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About Michael

Michael Malak has been implementing Spark solutions for two Fortune 200 companies since early 2013. He is currently at Oracle in Colorado in a team developing a Spark-based Big Data cloud app. He has an M.S. Math from George Mason University. His book Spark GraphX In Action is due to be published later in 2015.