CS748R: Final Project Report Probabilistic Latent Variable Models for Document Collections
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Geographical Latent Variable Models for Microblog Retrieval
Although topic models designed for textual collections annotated with geographical meta-data have been previously shown to be effective at capturing vocabulary preferences of people living in different geographical regions, little is known about their utility for information retrieval in general or microblog retrieval in particular. In this work, we propose simple and scalable geographical late...
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We propose a generative model for text and other collections of discrete data that generalizes or improves on several previous models including naive Bayes/unigram, mixture of unigrams [6], and Hofmann's aspect model , also known as probabilistic latent semantic indexing (pLSI) [3]. In the context of text modeling, our model posits that each document is generated as a mixture of topics, where t...
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Document clustering is one of the fundamental techniques of unsupervised learning from unstructured textual data which constitutes a real saving in terms of efficiency for various information retrieval (IR) tasks. The clustering results are not only used as basic information for the structure of a collection, but also as a preceding step before conducting other IR applications. On the other han...
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In many text collections, we encounter the scenario that a document contains multiple topics. Extracting such topics/subtopics/themes from the text collection is important for many text mining tasks, such as search result organization, subtopic retrieval, passage segmentation, document clustering, and contextual text mining. A well accepted practice is to explain the generation of each document...
متن کاملImproving document clustering in a learned concept space
Most document clustering algorithms operate in a high dimensional bag-of-words space. The inherent presence of noise in such representation obviously degrades the performance of most of these approaches. In this paper we investigate an unsupervised dimensionality reduction technique for document clustering. This technique is based upon the assumption that terms co-occurring in the same context ...
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تاریخ انتشار 2007