Natural Language Processing for Online Applications: Text Retrieval, Extraction & Categorization

By Peter Jackson, Isabelle Moulinier. 2002

344 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

On Attacking Statistical Spam Filters (2004, background): “The basic model is often known as the bag of words (multinomial) or multivariate model [6].”
Biomedical language processing: what's beyond PubMed? (2006, background): “For general-domain NLP, Jackson and Moulinier (2002) is an excellent overview of the field.”
Term Graph Model for Text Classification (2005, methodology): “Most existing text classification methods (and text mining methods at large) adopt the approach of transforming the text mining problem into traditional machine learning problem, where a large number of mature techniques can be applied [7, 14, 17, 18, 8].”
A shared task involving multi-label classification of clinical free text (2007, background): “On the other hand, micro-averaging may be preferred if the density of a class reﬂects its importance in the end-user system” (Jackson and Moulin-ier, 2002):160-161.”
Jigsaw: Supporting Investigative Analysis through Interactive Visualization (2007, background): “Obviously, an initial requirement for Jigsaw is to identify and extract the entities [8, 11, 14] from each report and, ideally, store them in a format that allows easier analysis and manipulation.”
Expertise identification and visualization from CVS (2008, background): “As long as there is some sort of formatting regularity, automated extraction is feasible [11].”