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Word2Vec Network Structure Explained
1. Word2Vec Network Structure Explained
Presented by: Subhashis Hazarika (The Ohio State University)
(Visualization Seminar Study)
2. Related Work
• Efficient Estimation of Word Representations in Vector Space – Mikolov et al.
2013
• Distributed Representations of Words and Phrases and their Compositionality –
Mikolov et al. 2013
• Linguistic Regularities in Continuous Space Word Representations – Mikolov et
al. 2013
• Implementation : https://code.google.com/archive/p/word2vec/ - Mikolov et al.
• word2vec Parameter Learning Explained – Rong 2014
• word2vec Explained: Deriving Mikolov et al’s Negative Sampling Word-
Embedding Method – Goldberg and Levy 2014
10. Context-based Representation
• Word is represented by context in use.
I eat an apple every day. eat | apple
Sometimes I like to eat orange as well. eat | orange
I like to drive my own car to work. drive | car
11. Context-based Representation Models
• Continuous Bag-of-words model (CBOW):
I eat an apple every day. eat, an, every, day | apple
• Skip-gram (SG):
I eat an apple every day. apple | eat, an, every, day
21. Context-based Representation Models
• Continuous Bag-of-words model (CBOW):
I eat an apple every day. eat, an, every, day | apple
• Skip-gram (SG):
I eat an apple every day. apple | eat, an, every, day