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.
| Identifer | oai:union.ndltd.org:uno.edu/oai:scholarworks.uno.edu:td-3003 |
| Date | 18 December 2014 |
| Creators | Handler, Abram |
| Publisher | ScholarWorks@UNO |
| Source Sets | University of New Orleans |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | University of New Orleans Theses and Dissertations |
| Rights | http://creativecommons.org/licenses/by/4.0/ |
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