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Tuesday, May 17 • 9:50am - 10:30am
Building Word2Vec Models with Text Data

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It is always amazing when someone is able to take a very hard and translate it to one that has been studied for centuries. This is the case with Word2Vec, which transforms words into vectors. Text is unstructured data and has been explored mathematically far less than vectors. Newton (1642-1726) may have been the first one to study vectors, while text mining started its studies a few decades ago. Word2Vec maps text to a vector space that can be utilized in a variety of ways, such as measuring distance between words. Therefore, given a word of interest, the aforementioned vector space can be used to compute the top N closest words. In this talk, I will explain how to build Word2Vec models with Twitter data stored in Hadoop using Spark and MLlib. I will describe how to choose the most important parameters to accurately train a Word2Vec matrix. In addition, I will show examples of how these models are used in practice in data products.

avatar for Jorge Castanon

Jorge Castanon

Lead Data Scientist, IBM
Jorge Castañón hails from Mexico City and received his Ph.D. in Computational and Applied Mathematics from Rice University. He has a genuine passion for data science and machine learning applications of any kind. Since 2007, he is been developing numerical optimization models and... Read More →

Tuesday May 17, 2016 9:50am - 10:30am PDT