  This paper evaluates existing and newly proposed answer selection methods
based on pre-trained word embeddings. Word embeddings are highly effective in
various natural language processing tasks and their integration into
traditional information retrieval (IR) systems allows for the capture of
semantic relatedness between questions and answers. Empirical results on three
publicly available data sets show significant gains over traditional term
frequency based approaches in both supervised and unsupervised settings. We
show that combining these word embedding features with traditional
learning-to-rank techniques can achieve similar performance to state-of-the-art
neural networks trained for the answer selection task.

Information Retrieval