Semanlink - Feature learning AND Unsupervised machine learning

Linear algebraic structure of word meanings – Off the convex path

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2018-09-20 About

[1601.03764] Linear Algebraic Structure of Word Senses, with Applications to Polysemy

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> Here it is shown that multiple word senses reside
in linear superposition within the word
embedding and simple sparse coding can recover
vectors that approximately capture the
senses

> Each extracted word sense is accompanied by one of about  2000 “discourse atoms” that gives a succinct description of which other words co-occur with that word sense.

> The success of the approach is mathematically explained using a variant of
the random walk on discourses model

("random walk": a generative model for language). Under the assumptions of this model,  there
exists a linear relationship between the vector of a
word w and the vectors of the words in its contexts (It is not the average of the words in w's context, but in a given corpus the matrix of the linear relationship does not depend on w. It can be estimated, and so we can compute the embedding of a word from the contexts it belongs to)

[Related blog post](/doc/?uri=https%3A%2F%2Fwww.offconvex.org%2F2016%2F07%2F10%2Fembeddingspolysemy%2F)

2018-08-28 About

Principal Component Analysis (PCA) for Feature Selection and some of its Pitfalls · Johannes Otterbach

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2018-03-05 About

Understanding Dimensionality Reduction and its Applications - Dataconomy

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2017-06-24 About

Computational Neuroscience: Why does the brain adopt mechanisms like sparse coding to represent the environment? - Quora

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2017-06-19 About

Bay Area Vision Meeting: Unsupervised Feature Learning and Deep Learning - YouTube