Abstract In Semantic Textual Similarity, systems rate the degree of semantic equivalence
between two text snippets. This year, the participants were challenged with new data sets for …

Textual similarity measurement is a challenging problem, as it requires understanding the
semantics of input sentences. Most previous neural network models use coarse-grained …

Owing to the need for a deep understanding of linguistic items, semantic representation is
considered to be one of the fundamental components of several applications in Natural …

One major deficiency of most semantic representation techniques is that they usually model
a word type as a single point in the semantic space, hence conflating all the meanings that …

Quantifying semantic similarity between linguistic items lies at the core of many applications
in Natural Language Processing and Artificial Intelligence. It has therefore received a …

Recognizing semantically similar sentences or paragraphs across languages is beneficial
for many tasks, ranging from cross-lingual information retrieval and plagiarism detection to …

Semantic textual similarity (STS) is the task of assessing the degree of similarity between
two texts in terms of meaning. Several approaches have been proposed in the literature to …

Abstract We present ExB Themis–a word alignmentbased semantic textual similarity system
developed for SemEval-2015 Task 2: Semantic Textual Similarity. It combines both string …

We describe an attention-based convolutional neural network for the English semantic
textual similarity (STS) task in the SemEval-2016 competition (Agirre et al., 2016). We …

We propose an approach for semantifying web extracted facts. In particular, we map subject
and object terms of these facts to instances; and relational phrases to object properties …