SRIUBC-Core: Multiword Soft Similarity Models for Textual Similarity

Abstract

In this year’s Semantic Textual Similarity evaluation, we explore the contribution of models that provide soft similarity scores across spans of multiple words, over the previous year’s system. To this end, we explored the use of neural probabilistic language models and a TF-IDF weighted variant of Explicit Semantic Analysis. The neural language model systems used vector representations of individual words, where these vectors were derived by training them against the context of words encountered, and thus reflect the distributional characteristics of their usage. To generate a similarity score between spans, we experimented with using tiled vectors and Restricted Boltzmann Machines to identify similar encodings. We find that these soft similarity methods generally outperformed our previous year’s systems, albeit they did not perform as well in the overall rankings. A simple analysis of the soft similarity resources over two word phrases is provided, and future areas of improvement are described.