Watset: Local-Global Graph Clustering with Applications in Sense and Frame Induction

Dmitry Ustalov, Alexander Panchenko, Chris Biemann, Simone Paolo Ponzetto


Abstract
We present a detailed theoretical and computational analysis of the Watset meta-algorithm for fuzzy graph clustering, which has been found to be widely applicable in a variety of domains. This algorithm creates an intermediate representation of the input graph, which reflects the “ambiguity” of its nodes. Then, it uses hard clustering to discover clusters in this “disambiguated” intermediate graph. After outlining the approach and analyzing its computational complexity, we demonstrate that Watset shows competitive results in three applications: unsupervised synset induction from a synonymy graph, unsupervised semantic frame induction from dependency triples, and unsupervised semantic class induction from a distributional thesaurus. Our algorithm is generic and can also be applied to other networks of linguistic data.
Anthology ID:
J19-3002
Volume:
Computational Linguistics, Volume 45, Issue 3 - September 2019
Month:
September
Year:
2019
Address:
Venue:
CL
SIG:
Publisher:
Note:
Pages:
423–479
Language:
URL:
https://www.aclweb.org/anthology/J19-3002
DOI:
10.1162/coli_a_00354
Bib Export formats:
BibTeX MODS XML EndNote
PDF:
http://aclanthology.lst.uni-saarland.de/J19-3002.pdf