  Time is an important relevance signal when searching streams of social media
posts. The distribution of document timestamps from the results of an initial
query can be leveraged to infer the distribution of relevant documents, which
can then be used to rerank the initial results. Previous experiments have shown
that kernel density estimation is a simple yet effective implementation of this
idea. This paper explores an alternative approach to mining temporal signals
with recurrent neural networks. Our intuition is that neural networks provide a
more expressive framework to capture the temporal coherence of neighboring
documents in time. To our knowledge, we are the first to integrate lexical and
temporal signals in an end-to-end neural network architecture, in which
existing neural ranking models are used to generate query-document similarity
vectors that feed into a bidirectional LSTM layer for temporal modeling. Our
results are mixed: existing neural models for document ranking alone yield
limited improvements over simple baselines, but the integration of lexical and
temporal signals yield significant improvements over competitive temporal
baselines.
