NDLI: Topic models and a revisit of text-related applications

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Topic models and a revisit of text-related applications

Content Provider	ACM Digital Library
Author	Ha-Thuc, Viet Srinivasan, Padmini
Abstract	Topic models such as aspect model or LDA have been shown as a promising approach for text modeling. Unlike many previous models that restrict each document to a single topic, topic models support the important idea that each document could be relevant to multiple topics. This makes topic models significantly more expressive in modeling text documents. However, we observe two limitations in topic models. One is that of scalability as it is extremely expensive to run the models on large corpora. The other limitation is the inability to model the key concept of relevance. This prevents the models from being directly applied to goals such as text classification and relevance feedback for query modification; in these goals, items relevant to topics (classes and queries) are provided upfront. The first aim of this paper is to sketch solutions for these limitations. To alleviate the scalability problem, we introduce a one-scan topic model requiring only a single pass over a corpus for inference. To overcome the latter, we propose relevance-based topic models that have the advantages of previous models while taking the concept of relevance into account. The second aim, based on the proposed models, is to revisit a wide range of well-known but still open text-related tasks, and outline our vision on how the approaches for the tasks could be improved by topic models.
Starting Page	25
Ending Page	32
Page Count	8
File Format	PDF
ISBN	9781605582573
DOI	10.1145/1458550.1458556
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2008-10-30
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Topic models Lda Gibbs sampling Relevance-based language models
Content Type	Text
Resource Type	Article

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