Automatic text classification is the task of organizing documents into pre-determined classes,
generally using machine learning algorithms. Generally speaking, it is one of the most …

Question classification is a key point in many applications, such as Question Answering (QA,
eg, Yahoo! Answers), Information Retrieval (IR, eg, Google search engine), and E-learning …

Text categorization plays a crucial role in both academic and commercial platforms due to
the growing demand for automatic organization of documents. Kernel-based classification …

One of the important problems in text classification is the high dimensionality of the feature
space. Feature selection methods are used to reduce the dimensionality of the feature space …

Abstract Vector Space Models (VSM) are commonly used in language processing to
represent certain aspects of natural language semantics. Semantics of VSM comes from the …

Natural Language Processing (NLP) is an interdisciplinary field between linguistics and
computer science. Its main aim is to process natural (human) language using computer …

In this study, we propose a novel methodology to build a semantic smoothing kernel to use
with Support Vector Machines (SVM) for text classification. The suggested approach is …

We propose a semantic kernel for Support Vector Machines (SVM) that takes advantage of
higher-order relations between the words and between the documents. Conventional …

The bag of words (BOW) representation of documents is very common in text classification
systems. However, the BOW approach ignores the position of the words in the document …

In this paper, we mainly study on n-gram models on text classification domain. In order to
measure impact of n-gram models on the classification performance, we carry out Naïve …