4 research outputs found
Inducing Language Networks from Continuous Space Word Representations
Recent advancements in unsupervised feature learning have developed powerful
latent representations of words. However, it is still not clear what makes one
representation better than another and how we can learn the ideal
representation. Understanding the structure of latent spaces attained is key to
any future advancement in unsupervised learning. In this work, we introduce a
new view of continuous space word representations as language networks. We
explore two techniques to create language networks from learned features by
inducing them for two popular word representation methods and examining the
properties of their resulting networks. We find that the induced networks
differ from other methods of creating language networks, and that they contain
meaningful community structure.Comment: 14 page
Topology of the conceptual network of language
We define two words in a language to be connected if they express similar
concepts. The network of connections among the many thousands of words that
make up a language is important not only for the study of the structure and
evolution of languages, but also for cognitive science. We study this issue
quantitatively, by mapping out the conceptual network of the English language,
with the connections being defined by the entries in a Thesaurus dictionary. We
find that this network presents a small-world structure, with an amazingly
small average shortest path, and appears to exhibit an asymptotic scale-free
feature with algebraic connectivity distribution.Comment: 4 pages, 2 figures, Revte