We describe three semantic text similarity systems developed for the *SEM 2013 STS shared task and the results of the corresponding three runs. All of them shared a word similarity feature that combined LSA word similarity and WordNet knowledge. The first, which achieved the best mean score of the 89 submitted runs, used a simple term alignment algorithm augmented with penalty terms. The other two runs, ranked second and fourth, used support vector regression models to combine larger sets of features.
UMBC_EBIQUITY REF is the first-ranked system participated in SemEval-2013, which uses a simple term alignment algorithm augmented with two penalty metrics.
UMBC_EBIQUITY-CORE: Semantic Textual Similarity Systems
*SEM
Computer Science