A Word Embeddings Model for Sentence Similarity

Abstract

Currently, word embeddings (Bengio et al, 2003; Mikolov et al, 2013) have had a major boom due to its performance in dierent Natural Language Processing tasks. This technique has overpassed many conventional methods in the literature. From the obtained embedding vectors, we can make a good grouping of words and surface elements. It is common to represent top-level elements such as sentences, using the idea of composition (Baroni et al, 2014) through vectors sum, vectors product or through dening a linear operator representing the composition. Here, we propose the representation of sentences through a matrix containing the word embedding vectors of such sentence. However, this involves obtaining a distance between matrices. To solve this, we use a Frobenius inner product. We show that this sentence representation overtakes traditional composition methods.