Foundations of statistical natural language processing
Foundations of statistical natural language processing
Feature Engineering for Text Classification
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Query Expansion with Long-Span Collocates
Information Retrieval
Automatically extracting and representing collocations for language generation
ACL '90 Proceedings of the 28th annual meeting on Association for Computational Linguistics
VizRank: Data Visualization Guided by Machine Learning
Data Mining and Knowledge Discovery
Language morphology offset: Text classification on a Croatian-English parallel corpus
Information Processing and Management: an International Journal
Automatic acquisition of inflectional lexica for morphological normalisation
Information Processing and Management: an International Journal
Visualization of text streams: a survey
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part II
Visualization of temporal text collections based on Correspondence Analysis
Expert Systems with Applications: An International Journal
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Explorative data analysis in text mining essentially relies on effective visualization techniques which can expose hidden relationships among documents and reveal correspondence between documents and their features. In text mining, the documents are most often represented by feature vectors of very high dimensions, requiring dimensionality reduction to obtain visual projections in two- or three-dimensional space. Correspondence analysis is an unsupervised approach that allows for construction of low-dimensional projection space with simultaneous placement of both documents and features, making it ideal for explorative analysis in text mining. Its present use, however, has been limited to word-based features. In this paper, we investigate how this particular document representation compares to the representation with letter n-grams and word n-grams, and find that these alternative representations yield better results in separating documents of different class. We perform our experimental analysis on a bilingual Croatian-English parallel corpus, allowing us to additionally explore the impact of features in different languages on the quality of visualizations.