UOW: semantically informed text similarity

Authors:
Miguel Rios;Wilker Aziz;Lucia Specia
Affiliations:
University of Wolverhampton, Wolverhampton, UK;University of Wolverhampton, Wolverhampton, UK;University of Sheffield, Regent Court, Portobello, Sheffield, UK
Venue:
SemEval '12 Proceedings of the First Joint Conference on Lexical and Computational Semantics - Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation
Year:
2012

Citing 6
Cited 0

Basic Algorithms and Operators

Basic Algorithms and Operators
Automatic retrieval and clustering of similar words

COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
BLEU: a method for automatic evaluation of machine translation

ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Findings of the 2010 Joint Workshop on Statistical Machine Translation and Metrics for Machine Translation

WMT '10 Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR
METEOR-NEXT and the METEOR paraphrase tables: improved evaluation support for five target languages

WMT '10 Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR
TINE: a metric to assess MT adequacy

WMT '11 Proceedings of the Sixth Workshop on Statistical Machine Translation

Quantified Score

Hi-index	0.00

Visualization

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.