Description: Abstract: This paper presents a method for measuring the semantic similarity of texts, using corpus-based and knowledge-based measures of similarity. Previous work on this problem has focused mainly on either large documents (e.g. text classification, information retrieval) or individual words (e.g. synonymy tests). Given that a large fraction of the information available today, on the Web and elsewhere, consists of short text snippets (e.g. abstracts of scientific documents, image captions, product descriptions), in this paper the authors focus on measuring the semantic similarity of short texts. Through experiments performed on a paraphrase data set, the authors show that the semantic similarity method out-performs methods based on simple lexical matching, resulting in up to 13% error rate reduction with respect to the traditional vector-based similarity metric.

Description: This paper presents a knowledge-based method for measuring the semantic-similarity of texts. While there is a large body of previous work focused on finding the semantic similarity of concepts and words, the application of these word-oriented methods to text similarity has not been yet explored. In this paper, the authors introduce a method that combines word-to-word similarity metrics into a text-to-text metric, and the authors show that this method outperforms the traditional text similarity metrics based on lexical matching.

Description: This paper discusses affective text. The "Affective Text" task focuses on the classification of emotions and valence (positive/negative polarity) in news headlines, and is meant as an exploration of the connection between emotions and lexical semantics. In this paper, the authors describe the data set used in the evaluation and the results obtained by the participating systems.