Query-oriented summarization aims at extracting an informative summary from a document collection for a given query. It is very useful to help users grasp the main information rel...
Most text mining methods are based on representing documents using a vector space model, commonly known as a bag of word model, where each document is modeled as a linear vector r...
Rowena Chau, Ah Chung Tsoi, Markus Hagenbuchner, V...
We consider the problem of document indexing and representation. Recently, Locality Preserving Indexing (LPI) was proposed for learning a compact document subspace. Different from...
Word fragments or n-grams have been widely used to perform different Natural Language Processing tasks such as information retrieval [1] [2], document categorization [3], automatic...
Traditionally, text classifiers are built from labeled training examples. Labeling is usually done manually by human experts (or the users), which is a labor intensive and time co...