The automated categorization (or classification) of texts into predefined categories has
witnessed a booming interest in the last 10 years, due to the increased availability of …

The past decade has seen a tremendous growth in the amount of experimental and
computational biomedical data, specifically in the areas of genomics and proteomics. This …

Current summarization systems yield generic summaries that are disconnected from users'
preferences and expectations. To address this limitation, we present CTRLsum, a novel …

Text mining is a new and exciting area of computer science research that tries to solve the
crisis of information overload by combining techniques from data mining, machine learning …

Text Classification, or the task of automatically assigning semantic categories to natural
language text, has become one of the key methods for organizing online information. Since …

The recent surge in the usage of social media has created an enormous amount of user‐
generated content (UGC). While there are streams of research that seek to mine UGC, these …

Text categorization presents unique challenges due to the large number of attributes present
in the data set, large number of training samples, attribute dependency, and multi-modality …

Memory-based language processing (MBLP) views language processing as being based on
the direct reuse of previous experience rather than on the use of rules or other structures …

The growing problem of unsolicited bulk e-mail, also known as “spam”, has generated a
need for reliable anti-spam e-mail filters. Filters of this type have so far been based mostly …

In this paper we present a simple linear-time centroid-based document classification
algorithm, that despite its simplicity and robust performance, has not been extensively …