Paper: SRIUBC-Core: Multiword Soft Similarity Models for Textual Similarity

ACL ID S13-1022
Title SRIUBC-Core: Multiword Soft Similarity Models for Textual Similarity
Venue Joint Conference on Lexical and Computational Semantics
Session
Year 2013
Authors

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 pre- vious year?s system. To this end, we ex- plored the use of neural probabilistic language models and a TF-IDF weighted variant of Ex- plicit 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 dis- tributional characteristics of their usage. To generate a similarity score between spans, we experimented with using tiled vectors and Re- stricted Boltzmann Machines to identify simi- lar encodings. We find that these soft similar- ity methods generally outperform...