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RUDOLF EREMYAN
MACHINE LEARNING SOFTWARE ENGINEER
WORD EMBEDDING LIBRARIES OVERVIEW:
Word2Vec && fastTEXT
CONTACTS: EREMYAN.RUDOLF@GMAIL.COM HTTPS://WWW.LINKEDIN.COM/IN/RUDOLFEREMYAN/
Ti bot for TBC bank
B Bot for TBC Insurance
Word Embedding?
Word embedding is the collective name for a set of language
modeling and feature learning techniques in natural language
processing (NLP) where words or phrases from the vocabulary
are mapped to vectors of real numbers. Conceptually it involves
a mathematical embedding from a space with one dimension
per word to a continuous vector space with much lower
dimension.
https://en.wikipedia.org/wiki/Word_embedding
Machine Translation
Chatbots
Ranking
Recommender Systems
Sentiment Analysis
How represent word as vector?
One-hot-encoding
“I love cake hate pizza”
Problems of one-hot-encoding
1.One-hot vectors are high-dimensional and sparse
2.Feature vector grows with the vocabulary size
3.Semantic and syntactic information are lost
WORD2VEC by Google
Efficient Estimation of Word Representations in Vector Space
by Mikolov, Corrado, Dean, Chen
2013 NAACL
https://arxiv.org/pdf/1301.3781.pdf
word2vec
word2vec
word2vec
word2vec. Visualization
word2vec
word2vec
Word2Vec Skip-gram
model
word2vec. skip-gram model
word2vec. skip-gram model
word2vec. skip-gram model
One-hot vector is almost all
zeros… what’s the effect of that?
word2vec. skip-gram model
One-hot vector is almost all zeros…
what’s the effect of that?
word2vec. skip-gram model
WORD2VEC LIMITATIONS???
WORD2VEC LIMITATIONS???
1. Doesn’t take into account the internal
structure of words
(bad for morphologically rich languages)
2.Out-of-Vocabulary cases for unseen
words
fastText by Facebook
Enriching Word Vectors with Subword Information
by Mikolov , Bojanowski, Grave, A. Joulin
2016
https://arxiv.org/abs/1607.04606
fastText
The main goal of the Fast Text embeddings is to
take into account the internal structure of words
while learning word representations – this is
especially useful for morphologically rich
languages, where otherwise the representations
for different morphological forms of words would
be learnt independently. The limitation becomes
even more important when these words occur
rarely.
N = 2
N = 4
student => ['st', 'tu', 'ud', 'de', 'en', 'nt']
student => ['stud', 'tude', 'uden', 'dent']
fastText
fastText differs from word2vec only in that it uses char
n-gram embeddings as well as the actual word
embedding in the scoring function to calculate scores
and then likelihoods for each word, given a context
word. In case char n-gram embeddings are not
present, this reduces (at least theoretically) to the
original word2vec model.
This can be implemented by setting 0 for the max
length of char n-grams for fastText.
WORD2VEC vs FastText
Comparision
fastText vs word2vec comparision
Syntactic test set Semantic test set
fastText vs word2vec comparision
fastText vs word2vec comparision
fastText vs word2vec comparision
TOOLS
https://radimrehurek.com/gensim/
https://fasttext.cc
• https://www.datascience.com/resources/notebooks/word-
embeddings-in-python
• http://mccormickml.com/2016/04/19/word2vec-tutorial-the-
skip-gram-model/
• https://rare-technologies.com/fasttext-and-gensim-word-
embeddings/
• https://www.gavagai.se/blog/2015/09/30/a-brief-history-of-
word-embeddings/
LINKS
QUESTIONS?

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GDG Tbilisi 2017. Word Embedding Libraries Overview: Word2Vec and fastText