Probabilistic Topic Maps: Navigating through Large Text Collections
T. Hofmann. Advances in Intelligent Data Analysis, (1999)
Abstract
The visualization of large text databases and document collections is an important step towards more flexible and interactive
types of information retrieval. This paper presents a probabilistic approach which combines a statistical, model—based analysiswith a topological visualization principle. Our method can be utilized to derive topic maps which represent topical information by characteristic keyword distributions arranged in a two—dimensional spatial layout.Combined with multi-resolution techniques this provides a three-dimensional space for interactive information navigation inlarge text collections.
%0 Journal Article
%1 hoffman-topic-maps
%A Hofmann, Thomas
%D 1999
%J Advances in Intelligent Data Analysis
%K maps navigation topic
%P 161--172
%T Probabilistic Topic Maps: Navigating through Large Text Collections
%U http://dx.doi.org/10.1007/3-540-48412-4_14
%X The visualization of large text databases and document collections is an important step towards more flexible and interactive
types of information retrieval. This paper presents a probabilistic approach which combines a statistical, model—based analysiswith a topological visualization principle. Our method can be utilized to derive topic maps which represent topical information by characteristic keyword distributions arranged in a two—dimensional spatial layout.Combined with multi-resolution techniques this provides a three-dimensional space for interactive information navigation inlarge text collections.
@article{hoffman-topic-maps,
abstract = {The visualization of large text databases and document collections is an important step towards more flexible and interactive
types of information retrieval. This paper presents a probabilistic approach which combines a statistical, model—based analysiswith a topological visualization principle. Our method can be utilized to derive topic maps which represent topical information by characteristic keyword distributions arranged in a two—dimensional spatial layout.Combined with multi-resolution techniques this provides a three-dimensional space for interactive information navigation inlarge text collections.},
added-at = {2008-08-03T02:51:47.000+0200},
author = {Hofmann, Thomas},
biburl = {https://www.bibsonomy.org/bibtex/22629087de41f480de8166ee5c4084ec0/pitman},
description = {SpringerLink - Book Chapter},
interhash = {d4cbb1544807077191fb3a4c76b0246b},
intrahash = {2629087de41f480de8166ee5c4084ec0},
journal = {Advances in Intelligent Data Analysis},
keywords = {maps navigation topic},
pages = {161--172},
timestamp = {2008-08-03T02:51:48.000+0200},
title = {Probabilistic Topic Maps: Navigating through Large Text Collections},
url = {http://dx.doi.org/10.1007/3-540-48412-4_14},
year = 1999
}