  Word Sense Induction (WSI) is the ability to automatically induce word senses
from corpora. The WSI task was first proposed to overcome the limitations of
manually annotated corpus that are required in word sense disambiguation
systems. Even though several works have been proposed to induce word senses,
existing systems are still very limited in the sense that they make use of
structured, domain-specific knowledge sources. In this paper, we devise a
method that leverages recent findings in word embeddings research to generate
context embeddings, which are embeddings containing information about the
semantical context of a word. In order to induce senses, we modeled the set of
ambiguous words as a complex network. In the generated network, two instances
(nodes) are connected if the respective context embeddings are similar. Upon
using well-established community detection methods to cluster the obtained
context embeddings, we found that the proposed method yields excellent
performance for the WSI task. Our method outperformed competing algorithms and
baselines, in a completely unsupervised manner and without the need of any
additional structured knowledge source.
