  Recently, doc2vec has achieved excellent results in different tasks. In this
paper, we present a context aware variant of doc2vec. We introduce a novel
weight estimating mechanism that generates weights for each word occurrence
according to its contribution in the context, using deep neural networks. Our
context aware model can achieve similar results compared to doc2vec initialized
byWikipedia trained vectors, while being much more efficient and free from
heavy external corpus. Analysis of context aware weights shows they are a kind
of enhanced IDF weights that capture sub-topic level keywords in documents.
They might result from deep neural networks that learn hidden representations
with the least entropy.
