  Community Question Answering is the field of computational linguistics that
deals with problems derived from the questions and answers posted to websites
such as Quora or Stack Overflow. Among some of these problems we find the issue
of ranking the multiple answers posted in reply to each question by how
informative they are in the attempt to solve the original question. This work
tries to advance the state of the art on answer ranking for community Question
Answering by proceeding with a deep learning approach. We started off by
creating a large data set of questions and answers posted to the Stack Overflow
website.
  We then leveraged the natural language processing capabilities of dense
embeddings and LSTM networks to produce a prediction for the accepted answer
attribute, and present the answers in a ranked form ordered by how likely they
are to be marked as accepted by the question asker. We also produced a set of
numerical features to assist with the answer ranking task. These numerical
features were either extracted from metadata found in the Stack Overflow posts
or derived from the questions and answers texts. We compared the performance of
our deep learning models against a set of forest and boosted trees ensemble
methods and found that our models could not improve the best baseline results.
We speculate that this lack of performance improvement versus the baseline
models may be caused by the large number of out of vocabulary words present in
the programming code snippets found in the questions and answers text. We
conclude that while a deep learning approach may be helpful in answer ranking
problems new methods should be developed to assist with the large number of out
of vocabulary words present in the programming code snippets
