Journal of Information Technology Research, 5(4), 85-98, October-December 2012 85 Copyright © 2012, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. Keywords: Formal Concept Analysis, Human Machine Interface, Morpheme Analysis, Natural Language Processing, Parser Modules INTRODUCTION Natural Language Processing (NLP) is an active area within human-machine interface develop- ment. The processing of input sentences given in human language or generating sentences of human language is still a challenging task in IT world. There are many problem areas in NLP where no standard solutions are available for every related task. The input sentences are processed in many different phases, where the usual process includes tokenization, cleaning, morpheme analysis, sentence analysis, semantic graph construction and sentence interpretation. The goal of the morpheme analysis module is to determine the stem of the word and to determine the grammatical role of the word within the sentence. The stem can be used to determine the concept related to the given word. Using some external ontology, the domain specific and universal knowledge elements can be extracted from the related external knowledge base. The ontology databases usually contain information on the specific relationships of the concepts like specialization, generalization, synonyms and specific application. The grammatical role of the words can be encoded on many ways. In some languages, the position of the word conveys the grammatical role. In some other languages, there is no dominant word order, thus other formal elements, like suffixes or prefixes are used to describe the role of the word. As a word may have several grammatical and semantic roles at the same time, several suffix or prefix parts can be attached to the stem word. The main goal of the morpheme analyzer module is to Classifcation Method for Learning Morpheme Analysis László Kovács, Department of Information Technology, University of Miskolc, Miskolc City, Hungary ABSTRACT The morpheme analysis module is an important component in natural language processing engines. The parser modules are usually based on rule systems created by human experts. In the paper, a novel approach is tested for implementation of the morpheme analyzer module. The proposed structure is based on the theory of formal concept analysis. The word infection can be considered as a classifcation problem, where the class label denotes the corresponding transformation rule. The main beneft of the proposed method is the effcient generalization feature. The proposed morpheme analyzer module was implemented in a prototype question generation application. DOI: 10.4018/jitr.2012100106