Short Texts Semantic Similarity Based on Word Embeddings

Evaluating semantic similarity of texts is a task that assumes paramount importance in real-world applications. In this paper, we describe some experiments we carried out to evaluate the performance of different forms of word embeddings and their aggregations in the task of measuring the similarity of short texts. In particular, we explore the results obtained with two publicly available pre-trained word embeddings (one based on word2vec trained on a specific dataset and the second extending it with embeddings of word senses). We test five approaches for aggregating words into text. Two approaches are based on centroids and summarize a text as a word embedding. The other approaches are some variations of the Okapi BM25 function and provide directly a measure of the similarity of two texts.