NDLI: Augmenting topic models with user relations in context based communication services

Content Provider	IEEE Xplore Digital Library
Author	Babu, V.T. Dhara, K.K. Krishnaswamy, V.
Copyright Year	2011
Description	Author affiliation: Department of Electrical Engineering, Indian Institute of Technology, Chennai, India 50036 (Babu, V.T.) \|\| IP Communication Research, Avaya Labs Research, Basking Ridge, NJ 07920, USA (Dhara, K.K.; Krishnaswamy, V.)
Abstract	Context-based communication services analyze user data and offer new and novel services that enhance end user unified communication experience. These services rely on data analysis and machine learning techniques to predict user behavior. In this paper we look at topic modeling as an unsupervised learning tool to categorize user communication data for retrieval. However, modeling topics based on user communication data, such as emails, meetings, invites, etc, poses several interesting challenges. One challenge is that user communication, even for a single topic, varies with the current context of the participating users. Other challenges include low lexical content and high contextual data in communication corpus. Hence, relying primarily on lexical analysis could result in inferior topic models. In this paper, we look at this problem of modeling topics for documents based on user communication. First, we use Latent Dirichlet Allocation (LDA) for extracting topics. LDA models documents as a mixture of latent topics where each topic consists of a probabilistic distribution over words. Then we use a technique that overlays a user-relational model over the lexical topic model generated by LDA. In this paper, we present our work and discuss our results.
Starting Page	1
Ending Page	10
File Size	370968
Page Count	10
File Format	PDF
ISBN	9781424489527
e-ISBN	9781424489534
e-ISBN	9781424489510
DOI	10.1109/COMSNETS.2011.5716478
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-01-04
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Context Analytical models Correlation Social network services Data models Electronic mail Context modeling
Content Type	Text
Resource Type	Article

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