Spectral Learning of Semantic Units in a Sentence Pair to Evaluate Semantic Textual Similarity

Abstract

Semantic Textual Similarity (STS) measures the degree of semantic equivalence between two snippets of text. It has applicability in a variety of Natural Language Processing (NLP) tasks. Due to the wide application range of STS in many fields, there is a constant demand for new methods as well as improvement in current methods. A surge of unsupervised and supervised systems has been proposed in this field but they pose a limitation in terms of scale. The restraints are caused either by the complex, non-linear sophisticated supervised learning models or by unsupervised learning models that employ a lexical database for word alignment. The model proposed here provides a spectral learning-based approach that is linear, scale-invariant, scalable, and fairly simple. The work focuses on finding semantic similarity by identifying semantic components from both the sentences that maximize the correlation amongst the sentence pair. We introduce an approach based on Canonical Correlation Analysis (CCA), using cosine similarity and Word Mover’s Distance (WMD) as a calculation metric. The model performs at par with sophisticated supervised techniques such as LSTM and BiLSTM and adds a layer of semantic components that can contribute vividly to NLP tasks.