Supervised Word Sense Disambiguation for Venetan: a Proof-of-Concept Experiment

Costanza Conforti and Alexander Fraser

FLAIRS 2017


Word Sense Disambiguation (WSD) is a classification task that consists
of determining which of the senses of an ambiguous word is activated
in a specific context. Research in this field has primarily
concentrated on investigating English and a few other well-resourced
languages. Recently, studies done on a corpus of Old English
(Wunderlich 2015) showed that, even with limited resources, it is
still possible to approach the problem of WSD. In this paper a WSD
system has been developed for the Low Resource Language (LRL) Venetan,
which has recently received some attention from the Natural Language
Processing (NLP) community. Our main contributions are twofold: first,
we select and annotate a corpus for Venetan, considering two words
(one abstract and one concrete term) and using two levels of
annotation (fine- and coarse-grained), reporting on annotator
agreement. Second, we report results of proof-of-concept experiments
of supervised WSD performed with Support Vector Machines on this
corpus. To our knowledge, our work is the first time that WSD for a
European Dialect like Venetan has been studied.