Amrita-CEN at SemEval-2016 task 1: Semantic relation from word embeddings in higher dimension

Semantic Textual Similarity measures similarity between pair of texts, even though the similar context is projected using different words. This work attempted to incorporate the context space of the sentence from that sentence alone. It proposes combination of Word2Vec and Non-Negative Matrix Factorization to represent the sentence as context embedding vector in context space. Distance and correlation values between context embedding vector pairs used as a features for Support Vector Regression to built the domain independent similarity measuring model. The proposed model yielding performance 0.41 in terms of correlation.