Saarland: Vector-based models of semantic textual similarity

HCTI at SemEval-2017 Task 1: Use convolutional neural network to evaluate Semantic Textual Similarity

This paper describes our convolutional neural network (CNN) system for Semantic Textual Similarity (STS) task. We calculated semantic similarity score between two sentences by comparing their semantic vectors. We generat…

Saarland at MRP 2019: Compositional parsing across all graphbanks

We describe the Saarland University submission to the shared task on Cross-Framework Meaning Representation Parsing (MRP) at the 2019 Conference on Computational Natural Language Learning (CoNLL).

Correlation Coefficients and Semantic Textual Similarity

A large body of research into semantic textual similarity has focused on constructing state-of-the-art embeddings using sophisticated modelling, careful choice of learning signals and many clever tricks. By contrast, lit…

PolyUCOMP: Combining Semantic Vectors with Skip bigrams for Semantic Textual Similarity

Determining Semantic Textual Similarity using Natural Deduction Proofs

Determining semantic textual similarity is a core research subject in natural language processing. Since vector-based models for sentence representation often use shallow information, capturing accurate semantics is diff…