Probabilistic Topic Maps: Navigating through Large Text Collections

@incollection{citeulike:1839829, 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 analysis with 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 in large text collections.}, author = {Hofmann, Thomas }, citeulike-article-id = {1839829}, journal = {Advances in Intelligent Data Analysis: Third International Symposium, IDA-99, Amsterdam, The Netherlands, August 1999. Proceedings}, keywords = {som, textmining}, pages = {161+}, posted-at = {2008-07-06 01:22:24}, priority = {2}, title = {Probabilistic Topic Maps: Navigating through Large Text Collections}, url = {http://www.springerlink.com/content/4fk7rkn2flpf1ppl }, year = {1999} }

TY - CHAP ID - citeulike:1839829 L3 - citeulike-article-id:1839829 N2 - 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 analysis with 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 in large text collections. JF - Advances in Intelligent Data Analysis: Third International Symposium, IDA-99, Amsterdam, The Netherlands, August 1999. Proceedings TI - Probabilistic Topic Maps: Navigating through Large Text Collections SP - 161 KW - som KW - textmining AU - Hofmann, Thomas PY - 1999/// UR - http://www.springerlink.com/content/4fk7rkn2flpf1ppl ER -