| Resumen |
This paper presents a novel approach for building
adaptive similarity functions based on cardinality using machine learning. Unlike current approaches
that build feature sets using similarity scores, we
have developed these feature sets with the cardinalities of the commonalities and differences between
pairs of objects being compared. This approach allows the machine-learning algorithm to obtain an
asymmetric similarity function suitable for directional judgments. Besides using the classic set cardinality, we used soft cardinality to allow ?exibility in
the comparison between words. Our approach used
only the information from the surface of the text,
a stop-word remover and a stemmer to address the
cross-lingual textual entailment task 8 at SEMEVAL
2012. We have the third best result among the 29
systems submitted by 10 teams. Additionally, this
paper presents better results compared with the best
of?cial score. |
