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Hierarchical Neural Language Models for Joint Representation of Streaming Documents and their Content
2015
Proceedings of the 24th International Conference on World Wide Web - WWW '15
We consider the problem of learning distributed representations for documents in data streams. The documents are represented as low-dimensional vectors and are jointly learned with distributed vector representations of word tokens using a hierarchical framework with two embedded neural language models. In particular, we exploit the context of documents in streams and use one of the language models to model the document sequences, and the other to model word sequences within them. The models
doi:10.1145/2736277.2741643
dblp:conf/www/DjuricWRGB15
fatcat:ikxtmpjscbennohgqm5w46tqvu