  Community question answering (CQA) forums are Internet-based platforms where
users ask questions about a topic and other expert users try to provide
solutions. Many CQA forums such as Quora, Stackoverflow, Yahoo!Answer,
StackExchange exist with a lot of user-generated data. These data are leveraged
in automated CQA ranking systems where similar questions (and answers) are
presented in response to the query of the user. In this work, we empirically
investigate a few aspects of this domain. Firstly, in addition to traditional
features like TF-IDF, BM25 etc., we introduce a BERT-based feature that
captures the semantic similarity between the question and answer. Secondly,
most of the existing research works have focused on features extracted only
from the question part; features extracted from answers have not been explored
extensively. We combine both types of features in a linear fashion. Thirdly,
using our proposed concepts, we conduct an empirical investigation with
different rank-learning algorithms, some of which have not been used so far in
CQA domain. On three standard CQA datasets, our proposed framework achieves
state-of-the-art performance. We also analyze importance of the features we use
in our investigation. This work is expected to guide the practitioners to
select a better set of features for the CQA retrieval task.
