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representation learning

Relevant articles

1 minute read


Recently, together with Jose Eliel Camargo I have been exploring a very nice and simple idea. When writing scientific articles, researchers put a lot of effort collecting relevant references and placing them within their text for different puposes: to give credit, to guide the reader to other points of view, to support some statement, etc. This means that looking for papers which tend to be cited close to each other in a collection of scientific articles should provide a good way to extract a group of similar or relevant articles.

Text embeddings in hyperbolic space

3 minute read


Here I review the idea of representation learning in hyperbolic space following [1-6]. I will focus on the application of these methods towards the generation of word embeddings from natural language in an unsupervised manner. The standard algorithms for generating word embeddings, such as word2vec or GloVe, generate word representations in a multidimensional Euclidean space. These have proven to be extremely useful for so called downstream tasks (such as text classification, word similarity and name entity recognition) due to their ability to capture semantic and syntactic relations among words when trained on large text corpora.