More interesting information from a DOD research lab that resulted in vectors <a href="https://www.kaggle.com/c/word2vec-nlp-tutorial/discussion/12349" rel="nofollow">https://www.kaggle.com/c/word2vec-nlp-tutorial/discussion/12...</a>
Recently HN featured a podcast interview with "one of the creators of Word2Vec and fastText", documenting some of his professional history.<p><a href="https://news.ycombinator.com/item?id=13630678" rel="nofollow">https://news.ycombinator.com/item?id=13630678</a>
This is an awesome explanation of those papers! Does anyone have any cool examples of word2vec being used in a project? I'd be interested in seeing what people could make with it.
For anyone looking for a simple javascript explorable explanation of this you can quickly download and run in a browser, I just found the following GitHub Project.<p>Demo:<p><a href="http://turbomaze.github.io/word2vecjson/" rel="nofollow">http://turbomaze.github.io/word2vecjson/</a><p>Code:<p><a href="https://github.com/turbomaze/word2vecjson" rel="nofollow">https://github.com/turbomaze/word2vecjson</a><p>The code looks pretty straightforward so I look forward to exploring this playground of a new and fascinating concept.
Word vectors are great. We've also written about them at length.[0] But any one interested in word vectors should also be looking at newer ways of applying neural nets to text. Specifically, convolutional nets with pooling for time are producing great results for clustering and classification.<p>[0] <a href="https://deeplearning4j.org//word2vec" rel="nofollow">https://deeplearning4j.org//word2vec</a>