Thesis Details
Word2vec modely s přidanou kontextovou informací
This thesis is concerned with the explanation of the word2vec models. Even though word2vec was introduced recently (2013), many researchers have already tried to extend, understand or at least use the model because it provides surprisingly rich semantic information. This information is encoded in N-dim vector representation and can be recall by performing some operations over the algebra. As an addition, I suggest a model modifications in order to obtain different word representation. To achieve that, I use public picture datasets. This thesis also includes parts dedicated to word2vec extension based on convolution neural network.
NLP, LM, AI, CNN, Natural Language Processing, Language Modelling, Word2vec, Word Embeddings, Artificial Neural Networks, Semantic Similarity, Softmax, Artificial Inteligence, Convolution Neural Network
Hrubý Martin, Ing., Ph.D. (DITS FIT BUT), člen
Janoušek Vladimír, doc. Ing., Ph.D. (DITS FIT BUT), člen
Jaroš Jiří, doc. Ing., Ph.D. (DCSY FIT BUT), člen
Matyska Luděk, prof. RNDr., CSc. (FI MUNI), člen
Peringer Petr, Dr. Ing. (DITS FIT BUT), člen
@mastersthesis{FITMT20124, author = "Martin \v{S}\r{u}stek", type = "Master's thesis", title = "Word2vec modely s p\v{r}idanou kontextovou informac\'{i}", school = "Brno University of Technology, Faculty of Information Technology", year = 2017, location = "Brno, CZ", language = "czech", url = "https://www.fit.vut.cz/study/thesis/20124/" }