Date of Award
Fall 12-2014
Degree Type
Thesis-Restricted
Department
Computer Science
Major Professor
Vassil Roussev
Second Advisor
Shengru Tu
Third Advisor
Tamjidul Hoque
Abstract
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.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Handler, Abram, "An empirical study of semantic similarity in WordNet and Word2Vec" (2014). University of New Orleans Theses and Dissertations. 1922.
https://scholarworks.uno.edu/td/1922
Included in
Artificial Intelligence and Robotics Commons, Computational Linguistics Commons, Other Computer Engineering Commons
Rights
The University of New Orleans and its agents retain the non-exclusive license to archive and make accessible this dissertation or thesis in whole or in part in all forms of media, now or hereafter known. The author retains all other ownership rights to the copyright of the thesis or dissertation.