Date of Award

Fall 12-18-2014

Degree Type



Computer Science

Major Professor

Vassil Roussev

Second Advisor

Shengru Tu

Third Advisor

Tamjidul Hoque


This thesis performs an empirical analysis of Word2Vec by comparing its output to WordNet, a well-known, human-curated lexical database. It finds that Word2Vec tends to uncover more of certain types of semantic relations than others -- with Word2Vec returning more hypernyms, synonomyns and hyponyms than hyponyms or holonyms. It also shows the probability that neighbors separated by a given cosine distance in Word2Vec are semantically related in WordNet. This result both adds to our understanding of the still-unknown Word2Vec and helps to benchmark new semantic tools built from word vectors.


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Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.