ICA and SOM in text document analysis
By Ella Bingham, Jukka Kuusisto, Krista Lagus. 2002
45 citations recorded by the source corpus.
In this study we show experimental results on using Independent Component Analysis (ICA) and the Self-Organizing Map (SOM) in document analysis. Our documents are segments of spoken dialogues carried out over the telephone in a customer service, transcribed into text. The task is to analyze the topics of the discussions, and to group the discussions into meaningful subsets. The quality of the grouping is studied by comparing to a manual topical classification of the documents.
Subfields: Artificial Intelligence, Signal Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Bernhard Schölkopf
- Francesco Locatello
- Dominik Janzing
- Luigi Gresele
- Kun Zhang
- Aapo Hyvärinen
- Yoshua Bengio
- Paul K. Rubenstein
In Signal Processing
- Bernhard Schölkopf
- Francesco Locatello
- Luigi Gresele
- Arash Mehrjou
- Aapo Hyvärinen
- Paul K. Rubenstein
- Jaakko J. Väyrynen
- Timo Honkela
Citations in the corpus
- The Incomplete Rosetta Stone problem: Identifiability results for Multi-view Nonlinear ICA (2019, background): “Its applications are ubiquitous, including neuroimaging [28], signal processing [34], text mining [5], astronomy [31] and financial time series analysis [32].”
- WordICA—emergence of linguistic representations for words by independent component analysis (2010, methodology): “This difference in the effect of using short versus long contexts is highlighted by comparing the results of earlier experiments using different methods (see, e.g., Bingham et al. 2001, 2002; Levy, Bullinaria and Patel 1998; Sahlgren 2006) and the results reported later in this paper.”