NDLI: Hierarchical Neural Language Models for Joint Representation of Streaming Documents and their Content

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Hierarchical Neural Language Models for Joint Representation of Streaming Documents and their Content

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Hierarchical Neural Language Models for Joint Representation of Streaming Documents and their Content

Content Provider	ACM Digital Library
Author	Grbovic, Mihajlo Djuric, Nemanja Radosavljevic, Vladan Wu, Hao Bhamidipati, Narayan
Abstract	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 learn continuous vector representations for both word tokens and documents such that semantically similar documents and words are close in a common vector space. We discuss extensions to our model, which can be applied to personalized recommendation and social relationship mining by adding further user layers to the hierarchy, thus learning user-specific vectors to represent individual preferences. We validated the learned representations on a public movie rating data set from MovieLens, as well as on a large-scale Yahoo News data comprising three months of user activity logs collected on Yahoo servers. The results indicate that the proposed model can learn useful representations of both documents and word tokens, outperforming the current state-of-the-art by a large margin.
Starting Page	248
Ending Page	255
Page Count	8
File Format	PDF
ISBN	9781450334693
DOI	10.1145/2736277.2741643
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-05-18
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Distributed representations Document modeling Machine learning Document embeddings Word embeddings
Content Type	Text
Resource Type	Article

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