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1 Introduction We describe our endeavour to build natural language processing (NLP) tools for a group of west Ugandan Bantu languages, Runyakitara. Orthographies for these languages were formalized no more than 150 years ago, and we have found that spelling errors are common in written Runyakitara, even by students in higher education and ‘professional’ users such as journalists. We therefore plan to build a spell-checker, RunyaSpeller, for Runyakitara which will help to improve writing accuracy, underpin general literacy, and support government policies in

1 Introduction In this paper, I describe the relationship of the field of typology to computational linguistics and natural language processing . Those latter two terms are sometimes used interchangeably to describe the field concerned with the processing of human language by computers. If a distinction is drawn between them, computational linguistics is used to describe research interested in answering linguistic questions using computational methodology, while natural language processing describes research on automatic processing of human language for

behaviors of a system. This article addresses the problem of generating a use case model from user requirements, written in Arabic, in a semiautomated approach. An Arabic natural language processing tool/software, namely Stanford Parser, is used to parse different statements of the user requirements, written in Arabic, to obtain lists of nouns, noun phrases, verbs, verb phrases, etc., that aid in finding potential actors and use cases. A set of steps that represent our approach for constructing a use case model are presented. The rest of the article is organized as

1. Natural Language Processing "Speaking and understanding the speech of others are things we do every day. Under normal circumstances we do these things effortlessly and, it seems, almost instantaneously. It takes almost no effort and very little, if any, conscious thought to turn our thoughts into words and sentences in order to communicate them to others; and, likewise, we ordinarily have no trouble in getting at the thoughts that others express in their words and sentences. " (Matthei/Roeper 1983: 13) The use of natural language is one of the most

Besim Kabashi A lexicon of Albanian for natural language processing 1 Introduction 2 Some notes on the Albanian language 3 A standard lexicon 4 Compiling an Albanian lexicon for the purposes of natural language processing 4.1 Improvements and work in the past 4.2 The idea 4.3 Parts-of-Speech and their subclassification Abstract: For many applications in the field of natural language processing, a lexi- con is needed. For the Albanian language a lexicon that can be used for these purposes is presented below. The lexicon contains around 75,000 entries, including

-Free Translation Models. ACL (Association for Computational Linguistics) Interactive Poster and Demonstration Sessions , 2010. Dyer, Chris, Victor Chahuneau, and Noah A Smith. A Simple, Fast, and Effective Reparameterization of IBM Model 2. NAACL (North American Association for Computational Linguistics) , 2013. Gao, Qin and Stephan Vogel. Parallel Implementations of Word Alignment Tool. Software Engineering, Testing, and Quality Assurance for Natural Language Processing , 2008. Heafield, Kenneth. KenLM : Faster and Smaller Language Model Queries. WMT (Workshop on

1 Introduction Lexicons are very important for many tasks in the field of natural language processing/human language technology, where either only part of the information is extracted or the unabridged dictionary is used. For the Albanian language there are many types of dictionaries nowadays, cf. Lloshi (1988), for an overview of the time before 1988. In the three decades since Lloshi’s report, new dictionaries or new types of dictionaries for Albanian have been compiled, e.g. synonym dictionaries, cf. Thomai et al. (2004), and Dhrimo et al. (2002), antonym

of 6th International Conference on Powerful ICT for Teaching and Learning, (pp. 23-26). St.Petersburg. Hearst, M. (2005). Teaching Applied Natural Language Processing: Triumphs and Tribulations . TeachNLP ‘05 Proceedings of the Second ACL Workshop on Effective Tools and Methodologies for Teaching Natural Language Processing and Computational Linguistics, (pp. 1-8). Ian, H., Peter, P.-S. F., Harold, B., Said, T., Benjamin, G., & Mike, D. (2004). SWRL: A Semantic Web Rule Language . John, S. (2014). Development of an Educational Ontology for Java Programming (JLEO

Chapter 4 Natural Language Processing 4.1 Natural Language Processing (NLP) The idea of natural language processing (NLP) is to develop a computer system that can analyze, understand and generate natural human language. There are many applications in NLP including topic modeling, customer sentiment analysis, ma- chine translation and concept search. There are four categories of NLP techniques: 1) Pattern matching: This approach attempts to interpret input text by patterns rather than combining the structure and meaning of words. 2) Syntactically driven parsing