Word2Vec is a recent breakthrough in the world of NLP. Tomas Mikolov a Czech computer scientist and currently a researcher at CIIRC ( Czech Institute of Informatics, Robotics and Cybernetics) was one…
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NASA has been set the ambitious targets of taking humans back to the Moon by 2024, then to later make the order-of-magnitude leap on to Mars. Even for the globally renowned space agency, it is a struggle.
According to a recent report by the Government Accountability Office (GAO) it still isn t quite sure how much this will cost, nor how long it will take to get the equipment ready. Hardware challenges abound, including testing an electric ion propulsion system for the orbiting capsule, dubbed
Gateway.
On top of all that, there is the question of whether NASA has the right people in place to take on the Moon mission and the trip to the Red Planet beyond. Trying to come up with some answers is David Meza, senior data scientist within the agency’s people analytics group, who has begun employing graph database technologies to try and help.
Rationale
Although continuous bag of word (CBOW) embeddings can be trained more quickly than skipgram (SG) embeddings, it is a common belief that SG embeddings tend to perform better in practice. This was observed by the original authors of Word2Vec [1] and also in subsequent work [2]. However, we found that popular implementations of word2vec with negative sampling such as word2vec and gensim do not implement the CBOW update correctly, thus potentially leading to misconceptions about the performance of CBOW embeddings when trained correctly.
We release kōan so that others can efficiently train CBOW embeddings using the corrected weight update. See this technical report for benchmarks of kōan vs. gensim word2vec negative sampling implementations. If you use kōan to learn word embeddings for your own work, please cite: