  In the area of community question answering (CQA), answer selection and
answer ranking are two tasks which are applied to help users quickly access
valuable answers. Existing solutions mainly exploit the syntactic or semantic
correlation between a question and its related answers (Q&A), where the
multi-facet domain effects in CQA are still underexplored. In this paper, we
propose a unified model, Enhanced Attentive Recurrent Neural Network (EARNN),
for both answer selection and answer ranking tasks by taking full advantages of
both Q&A semantics and multi-facet domain effects (i.e., topic effects and
timeliness). Specifically, we develop a serialized LSTM to learn the unified
representations of Q&A, where two attention mechanisms at either sentence-level
or word-level are designed for capturing the deep effects of topics. Meanwhile,
the emphasis of Q&A can be automatically distinguished. Furthermore, we design
a time-sensitive ranking function to model the timeliness in CQA. To
effectively train EARNN, a question-dependent pairwise learning strategy is
also developed. Finally, we conduct extensive experiments on a real-world
dataset from Quora. Experimental results validate the effectiveness and
interpretability of our proposed EARNN model.
