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Introduction to word embeddings with Python

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Pavel Kalaidin
Pavel Kalaidin

My talk at Moscow Data Fest, 2015

Data & Analytics
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Introduction to word embeddings Pavel Kalaidin @facultyofwonder Moscow Data Fest, September, 12th, 2015
distributional hypothesis
лойс
годно, лойс лойс за песню из принципа не поставлю лойс взаимные лойсы лойс, если согласен What is the meaning of лойс?
годно, лойс лойс за песню из принципа не поставлю лойс взаимные лойсы лойс, если согласен What is the meaning of лойс?
кек
кек, что ли? кек))))))) ну ты кек What is the meaning of кек?
кек, что ли? кек))))))) ну ты кек What is the meaning of кек?
vectorial representations of words
simple and flexible platform for understanding text and probably not messing up
one-hot encoding? 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
co-occurrence matrix recall: word-document co-occurrence matrix for LSA
credits: [x]
from entire document to window (length 5-10)
still seems suboptimal -> big, sparse, etc.
lower dimensions, we want dense vectors (say, 25-1000)
How?
matrix factorization?
SVD of co-occurrence matrix
lots of memory?
idea: directly learn low- dimensional vectors
here comes word2vec Distributed Representations of Words and Phrases and their Compositionality, Mikolov et al: [paper]
idea: instead of capturing co- occurrence counts predict surrounding words
Two models: C-BOW predicting the word given its context skip-gram predicting the context given a word Explained in great detail here, so we’ll skip it for now Also see: word2vec Parameter Learning Explained, Rong, paper
CBOW: several times faster than skip-gram, slightly better accuracy for the frequent words Skip-Gram: works well with small amount of data, represents well rare words or phrases
Examples?
Wwoman - Wman = Wqueen - Wking classic example
<censored example>
word2vec Explained: Deriving Mikolov et al.’s Negative-Sampling Word-Embedding Method, Goldberg et al, 2014 [arxiv]
all done with gensim: github.com/piskvorky/gensim/
...failing to take advantage of the vast amount of repetition in the data
so back to co-occurrences
GloVe for Global Vectors Pennington et al, 2014: nlp.stanford. edu/pubs/glove.pdf
Ratios seem to cancel noise
The gist: model ratios with vectors
The model
Preserving linearity
Preventing mixing dimensions
Restoring symmetry, part 1
recall:
Restoring symmetry, part 2
Least squares problem it is now
SGD->AdaGrad
ok, Python code
glove-python: github.com/maciejkula/glove-python
two sets of vectors input and context + bias average/sum/drop
complexity |V|2
complexity |C|0.8
Evaluation: it works
#spb #gatchina #msk #kyiv #minsk #helsinki
Compared to word2vec
#spb #gatchina #msk #kyiv #minsk #helsinki
t-SNE: github.com/oreillymedia/t-SNE-tutorial seaborn: stanford.edu/~mwaskom/software/seaborn/
Abusing models
music playlists: github.com/mattdennewitz/playlist-to-vec
deep walk: DeepWalk: Online Learning of Social Representations [link]
user interests Paragraph vectors: cs.stanford. edu/~quocle/paragraph_vector.pdf
predicting hashtags interesting read: #TAGSPACE: Semantic Embeddings from Hashtags [link]
RusVectōrēs: distributional semantic models for Russian: ling.go.mail. ru/dsm/en/
corpus matters
building block for bigger models ╰(*´︶`*)╯
</slides>

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