During the 20th century biology became a pilot science; many disciplines have formulated their theories around models taken from biology. Computer science has become an almost bio-inspired field, thanks to the great developments in natural computing and DNA computing. In linguistics there have been several attempts to establish structural parallelisms between DNA sequences and verbal language. In general, it can be stated that formal languages and natural language processing (NLP) can take great advantage of the structural and “semantic” similarities with the genetic code. NLP could become another “bio-inspired” science, by using methods taken from theoretical computer science, which provides bio-inspired tools and formalizations. Therefore, a theoretical framework could be obtained where biology, NLP, and computer science exchange models and interact. Artificial intelligence can take advantage of the collaboration between these three sciences by providing the scientific
environment where this fruitful exchange can be developed. A main objective of the International Workshop on AI methods for Interdisciplinary Research in Language and Biology is to encourage and promote the exchange of knowledge among specialists dedicated to linguistics, biology and computation—specialists who are particularly interested in using methods from other disciplines that can help improve their models and theories, as well as bring new ideas, tools, and formalisms to their research. Among the methods relevant for such interdisciplinary research, the following are considered highly interesting for those working in the fields of research that this workshop covers: a) Modelling cognitive capabilities for producing language. b) Modelling tools for verbal language and nucleic acid language comprehension. c) Modelling human learning to achieve automatic learning. d) Modelling language evolution. e) Modelling computational and mathematical tools for natural language processing The workshop features one paper dealing with the first of our main focus: the modelling of cognitive capabilities for producing language. Dariusz Plewczynski offers a view on the Design and Modeling of Cognitive Agents. The second line of interest, modelling tools for verbal language and nucleic acid language comprehension, has a paper that stresses the capacity of formal languages to deal with Bio-Molecular structures: On Representing Natural Languages and Bio-Molecular Structures Using Matrix Insertion-Deletion Systems and Its Computational Completeness, by Kuppusamy Mahendran and Narayanan. Three more papers offer information on implementations or computational applications for natural language, the mining of medical texts, and speech segmentation: NEPsLingua: A New Textual Language To Program NEPs, by Ortega et al.; Parsing Medical Text into De-identified Databases, by Dahl et al.; and Vowel-Consonant Speech Segmentation by Neuromorphic Units, by Gómez-Vilda et al. Another paper discusses the use of human learning as a model to describe and achieve automatic machine learning. Becerra-Bonache and Jiménez-López present the article Children as Models for Computers: Natural Language Acquisition for Machine Learning. The research line of language evolution is discussed in the paper A Grammatical View of Language Evolution, by Bel-Enguix, Christiansen and Jiménez-López. Finally, two papers deal with the modeling of mathematical tools for understanding formal and natural languages. Loukanova presents Minimal Recursion Semantics and the Language of Acyclic Recursion, and Nagy and Otto present Finite-State Acceptors with Translucent Letters. We want to acknowledge the organizers of the main conference ICAART, for their kind help in managing the workshop; the program committee, for their fast and accurate reviewing; and the authors who submitted their work to this scientific meeting. All of them have contributed to improving the collaboration between biology, linguistics and computer science in the framework of artificial intelligence.
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