Natural Language Engineering 17 (2): 141–144. c  Cambridge University Press 2011 doi:10.1017/S1351324911000015 141 Finite-state methods and models in natural language processing ANSSIYLI-JYR ¨ A 1 , ANDR ´ AS KORNAI 2 and JACQUES SAKAROVITCH 3 1 Department of Modern Languages, PO Box 24, 00014 University of Helsinki, Finland email: anssi.yli-jyra@helsinki.fi 2 Computer and Automation Research Institute, Hungarian Academy of Sciences, Kende u 13-17, Budapest 1111, Hungary and Harvard University, Institute for Quantitative Social Science, 1737 Cambridge St, Cambridge MA 02138, USA email: andras@kornai.com 3 CNRS and Laboratoire Traitement et Communication de l’Information, Telecom ParisTech, 46, rue Barrault, 75634 Paris Cedex 13, France email: sakarovitch@enst.fr (Received 20 December 2010 ) 1 Introduction For the past two decades, specialised events on finite-state methods have been successful in presenting interesting studies on natural language processing to the public through journals and collections. The FSMNLP workshops have become well-known among researchers and are now the main forum of the Association for Computational Linguistics’ (ACL) Special Interest Group on Finite-State Methods (SIGFSM). The current issue on finite-state methods and models in natural language processing was planned in 2008 in this context as a response to a call for special issue proposals. In 2010, the issue received a total of sixteen submissions, some of which were extended and updated versions of workshop papers, and others which were completely new. The final selection, consisting of only seven papers that could fit into one issue, is not fully representative, but complements the prior special issues in a nice way. The selected papers showcase a few areas where finite-state methods have less than obvious and sometimes even groundbreaking relevance to natural language processing (NLP) applications. These methods have grown around Kleene’s classical result that relates languages defined by regular expressions to languages defined by finite automata (Kleene 1956). Practical finite-state methods apply and extend this correspondence. The most obvious extensions beyond the ordinary finite automata include finite transducers. Weighted automata provide another natural extension that potentially brings unde- cidability to the picture if weights are defined carelessly. However, typical definitions for weights are quite practical in applications as witnessed by the success of weighted transducers in automatic speech recognition. It should also be kept in mind that