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

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This presentation about comparing different word embedding models and libraries like word2vec and fastText, describing their difference and showing pros and cons.

Ingenieurwesen

RUDOLF EREMYAN
MACHINE LEARNING SOFTWARE ENGINEER
WORD EMBEDDING LIBRARIES OVERVIEW:
Word2Vec && fastTEXT
CONTACTS: EREMYAN.RUDOLF@GMAIL.COM HTTPS://WWW.LINKEDIN.COM/IN/RUDOLFEREMYAN/

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

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. skip-gram model
One-hot vector is almost all
zeros… what’s the eﬀect of that?

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.

fastText vs word2vec comparision
Syntactic test set Semantic test set

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

1. RUDOLF EREMYAN MACHINE LEARNING SOFTWARE ENGINEER WORD EMBEDDING LIBRARIES OVERVIEW: Word2Vec && fastTEXT CONTACTS: EREMYAN.RUDOLF@GMAIL.COM HTTPS://WWW.LINKEDIN.COM/IN/RUDOLFEREMYAN/

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4. 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

5. Machine Translation

6. Chatbots

7. Ranking

8. Recommender Systems

9. Sentiment Analysis

10. How represent word as vector?

11. One-hot-encoding “I love cake hate pizza”

12. 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

13. 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

14. word2vec

15. word2vec

16. word2vec

17. word2vec. Visualization

18. word2vec

19. word2vec

20. Word2Vec Skip-gram model

21. word2vec. skip-gram model

22. word2vec. skip-gram model

23. word2vec. skip-gram model One-hot vector is almost all zeros… what’s the eﬀect of that?

24. word2vec. skip-gram model One-hot vector is almost all zeros… what’s the eﬀect of that?

25. word2vec. skip-gram model

26. WORD2VEC LIMITATIONS???

27. 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

28. fastText by Facebook Enriching Word Vectors with Subword Information by Mikolov , Bojanowski, Grave, A. Joulin 2016 https://arxiv.org/abs/1607.04606

29. 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']

30. 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.

31. WORD2VEC vs FastText Comparision

32. fastText vs word2vec comparision Syntactic test set Semantic test set

33. fastText vs word2vec comparision

34. fastText vs word2vec comparision

35. fastText vs word2vec comparision

36. TOOLS https://radimrehurek.com/gensim/ https://fasttext.cc

37. • 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

38. QUESTIONS?