  Vector representation of sentences is important for many text processing
tasks that involve clustering, classifying, or ranking sentences. Recently,
distributed representation of sentences learned by neural models from unlabeled
data has been shown to outperform the traditional bag-of-words representation.
However, most of these learning methods consider only the content of a sentence
and disregard the relations among sentences in a discourse by and large.
  In this paper, we propose a series of novel models for learning latent
representations of sentences (Sen2Vec) that consider the content of a sentence
as well as inter-sentence relations. We first represent the inter-sentence
relations with a language network and then use the network to induce contextual
information into the content-based Sen2Vec models. Two different approaches are
introduced to exploit the information in the network. Our first approach
retrofits (already trained) Sen2Vec vectors with respect to the network in two
different ways: (1) using the adjacency relations of a node, and (2) using a
stochastic sampling method which is more flexible in sampling neighbors of a
node. The second approach uses a regularizer to encode the information in the
network into the existing Sen2Vec model. Experimental results show that our
proposed models outperform existing methods in three fundamental information
system tasks demonstrating the effectiveness of our approach. The models
leverage the computational power of multi-core CPUs to achieve fine-grained
computational efficiency. We make our code publicly available upon acceptance.
