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Probabilistic latent semantic analysis

About: Probabilistic latent semantic analysis is a research topic. Over the lifetime, 2884 publications have been published within this topic receiving 198341 citations. The topic is also known as: PLSA.


Papers
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Book ChapterDOI
22 May 2007
TL;DR: This paper uses probabilistic techniques to detect keywords from corporate blogs with respect to certain topics, and demonstrates how this method can present the blogosphere in terms of topics with measurable keywords, hence tracking popular conversations and topics in theBlogosphere.
Abstract: With the proliferation of blogs, or weblogs, in the recent years, information in the blogosphere is becoming increasingly difficult to access and retrieve. Previous studies have focused on analyzing personal blogs, but few have looked at corporate blogs, the numbers of which are dramatically rising. In this paper, we use probabilistic techniques to detect keywords from corporate blogs with respect to certain topics. We then demonstrate how this method can present the blogosphere in terms of topics with measurable keywords, hence tracking popular conversations and topics in the blogosphere. By applying a probabilistic approach, we can improve information retrieval in blog search and keywords detection, and provide an analytical foundation for the future of corporate blog search and mining.

18 citations

Book ChapterDOI
11 Sep 2005
TL;DR: This paper proposes the use of a neural network based, non-probabilistic, solution, which captures jointly a rich representation of words and documents, as compared to the classical TFIDF representations.
Abstract: Text categorization and retrieval tasks are often based on a good representation of textual data. Departing from the classical vector space model, several probabilistic models have been proposed recently, such as PLSA. In this paper, we propose the use of a neural network based, non-probabilistic, solution, which captures jointly a rich representation of words and documents. Experiments performed on two information retrieval tasks using the TDT2 database and the TREC-8 and 9 sets of queries yielded a better performance for the proposed neural network model, as compared to PLSA and the classical TFIDF representations.

18 citations

Proceedings Article
01 Jan 2008
TL;DR: A methodology to combine two different techniques for Semantic Relation Extraction from texts by filtering the candidate relations obtained through generic lexico-syntactic patterns and by labelling the anonymous Relations obtained through the statistical system.
Abstract: We describe here a methodology to combine two different techniques for Semantic Relation Extraction from texts. On the one hand, generic lexicosyntactic patterns are applied to the linguistically analyzed corpus to detect a first set of pairs of co-occurring words, possibly involved in “syntagmatic” relations. On the other hand, a statistical unsupervised association system is used to obtain a second set of pairs of “distributionally similar” terms, that appear to occur in similar contexts, thus possibly involved in “paradigmatic” relations. The approach aims at learning ontological information by filtering the candidate relations obtained through generic lexico-syntactic patterns and by labelling the anonymous relations obtained through the statistical system. The resulting set of relations can be used to enrich existing ontologies and for semantic annotation of documents or web pages.

18 citations

Proceedings ArticleDOI
16 Dec 2008
TL;DR: This paper uses video epitomes for segmenting foreground objects from background and applies pLSA for finding correlations among these patches to learn usual activities in the scene and extends it to classify a novel video as usual or unusual.
Abstract: In this paper, we address the problem of unsupervised learning of usual patterns of activities in an area under surveillance and detecting deviant patterns We use video epitomes for segmenting foreground objects from background and obtain an approximate shape, trajectory and temporal information in the form of space-time patches We apply pLSA for finding correlations among these patches to learn usual activities in the scene We also extend pLSA to classify a novel video as usual or unusual

18 citations

Proceedings ArticleDOI
29 Aug 2016
TL;DR: It is shown that the abstraction into a qualitative space helps the robot to generalise and compare multiple noisy and partial observations in a real world dataset and that a vocabulary of latent activity classes (expressed using qualitative features) can be recovered.
Abstract: We show that by using qualitative spatio-temporal abstraction methods, we can learn common human movements and activities from long term observation by a mobile robot. Our novel framework encodes multiple qualitative abstractions of RGBD video from detected activities performed by a human as encoded by a skeleton pose estimator. Analogously to informational retrieval in text corpora, we use Latent Semantic Analysis (LSA) to uncover latent, semantically meaningful, concepts in an unsupervised manner, where the vocabulary is occurrences of qualitative spatio-temporal features extracted from video clips, and the discovered concepts are regarded as activity classes. The limited field of view of a mobile robot represents a particular challenge, owing to the obscured, partial and noisy human detections and skeleton pose-estimates from its environment. We show that the abstraction into a qualitative space helps the robot to generalise and compare multiple noisy and partial observations in a real world dataset and that a vocabulary of latent activity classes (expressed using qualitative features) can be recovered.

18 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202319
202277
202114
202036
201927
201858