Publications

Abstract : Comparing the semantics of a pair of sentences has been an interesting yet unstructured problem. Semantic analysis is mostly elusive due to the fact that the semantics of Natural language constructs cannot be measured, let alone be compared to one another. Methods like Latent Semantic Analysis(LSA) and Latent Dichlaret Analysis(LDA) are able to capture broader semantics between documents, but their contribution in pairwise comparison tasks which require deeper semantics may be limited. In this paper we present a local alignment based scoring scheme for sentence pairs using word embeddings and how this can be used as a feature for some popular text analysis tasks such as summarization, paraphrase comparison, topic profiling and other semantic comparison tasks. We also present a theoretical analysis on the metrics used in this approach and a separability argument using t-SNE plots. Furthermore we detail our Spark implementation model for the pairwise comparison and summarization.