UOW: Semantically Informed Text Similarity

Miguel Angel Rios Gaona

Abstract:

The UOW submissions to the Semantic Textual Similarity task at SemEval-2012 use a
supervised machine learning algorithm along
with features based on lexical, syntactic and
semantic similarity metrics to predict the semantic equivalence between a pair of sentences. The lexical metrics are based on word overlap. A shallow syntactic metric is based
on the overlap of base-phrase labels. The
semantically informed metrics are based on
the preservation of named entities and on the
alignment of verb predicates and the overlap
of argument roles using inexact matching. Our
submissions outperformed the official baseline, with our best system ranked above average, but the contribution of the semantic metrics was not conclusive.