KLUE-CORE: A regression model of semantic textual similarity
Paul Greiner, Thomas Proisl, Stefan Evert, Besim Kabashi · 2013
This paper describes our system entered for the *SEM 2013 shared task on Semantic Textual Similarity (STS). We focus on the core task of predicting the semantic textual similarity of sentence pairs. The current system utilizes machine learning techniques trained on semantic similarity ratings from the *SEM 2012 shared task; it achieved rank 20 out of 90 submissions from 35 different teams. Given the simple nature of our approach, which uses only WordNet and unannotated corpus data as external resources, we consider this a remarkably good result, making the system an interesting tool for a wide range of practical applications. 1