Linguistic models at the crossroads of agents, learning and formal languages

This paper aims at reviewing the most relevant linguistic applications developed in the intersection between three different fields: machine learning, formal language theory and agent technologies. On the one hand, we present some of the main linguistic contributions of the intersection between machine learning and formal languages, which constitutes a well-established research area known as Grammatical Inference. On the other hand, we present an overview of the main linguistic applications of models developed in the intersection between agent technologies and formal languages, such as colonies, grammar systems and eco-grammar systems. Our goal is to show how interdisciplinary research between these three fields can contribute to better understand how natural language is acquired and processed.

[1]  E. Mark Gold,et al.  Language Identification in the Limit , 1967, Inf. Control..

[2]  Stuart M. Shieber,et al.  Evidence against the context-freeness of natural language , 1985 .

[3]  Ryo Yoshinaka,et al.  Distributional learning of parallel multiple context-free grammars , 2013, Machine Learning.

[4]  Enrique Vidal,et al.  Inference of k-Testable Languages in the Strict Sense and Application to Syntactic Pattern Recognition , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Stanley Peters,et al.  Cross-Serial Dependencies in Dutch , 1982 .

[6]  Yasubumi Sakakibara,et al.  Efficient Learning of Context-Free Grammars from Positive Structural Examples , 1992, Inf. Comput..

[7]  Angelo Cangelosi,et al.  Computersimulation:anewscientific Approachtothestudyoflanguageevolution Anewapproachtothestudyoflanguage Evolution:computersimulation , 2001 .

[8]  Leonor Becerra Bonache.  On the learnibility of Mildly Context-Sensitive languages using positive data and correction queries , 2006 .

[9]  Leonor Becerra-Bonache,et al.  Learning Meaning Before Syntax , 2008, ICGI.

[10]  Leonor Becerra-Bonache,et al.  Effects of Meaning-Preserving Corrections on Language Learning , 2011, CoNLL.

[11]  J. Sadock.  Autolexical Syntax: A Theory of Parallel Grammatical Representations , 1990 .

[12]  Frederick J. Newmeyer,et al.  Generative Linguistics: An Historical Perspective , 1995 .

[13]  David Reitter,et al.  Did Social Networks Shape Language Evolution? A Multi-Agent Cognitive Simulation , 2010, CMCL@ACL.

[14]  Erzsébet Csuhaj-Varjú,et al.  Eco-Grammar Systems: A Grammatical Framework for Studying Lifelike Interactions , 1997, Artificial Life.

[15]  M. Dolores Jiménez López,et al.  A grammar systems approach to natural language grammar , 2006 .

[16]  Dimitar Kazakov,et al.  The Role of Environment Structure in Multi-Agent Simulations of Language Evolution , 2004 .

[17]  Hubert Cuyckens,et al.  The Oxford handbook of cognitive linguistics , 2010 .

[18]  Morten H. Christiansen,et al.  Language evolution: consensus and controversies , 2003, Trends in Cognitive Sciences.

[19]  Alaa A. Kharbouch,et al.  Three models for the description of language , 1956, IRE Trans. Inf. Theory.

[20]  Mark Steedman,et al.  Dependency and Coordination in the Grammar of Dutch and English , 1985 .

[21]  A. Mateescu,et al.  Contexts and the Concept of Mild Context-Sensitivity , 2003 .

[22]  Leonor Becerra-Bonache,et al.  Inferring Grammars for Mildly Context Sensitive Languages in Polynomial-Time , 2006, ICGI.

[23]  Leonor Becerra-Bonache,et al.  Learning Mild Context-Sensitiveness: Toward Understanding Children's Language Learning , 2004, ICGI.

[24]  Andrew Roberts,et al.  A Multilingual Parallel Parsed Corpus as Gold Standard for Grammatical Inference Evaluation , 2004 .

[25]  Maria Dolores Jiménez-López,et al.  A Grammar-Based Multi-Agent System for Language Evolution , 2012, PAAMS.

[26]  Geoffrey K. Pullum,et al.  Computationally Relevant Properties of Natural Languages and Their Grammars , 1985 .

[27]  Beatrice Santorini,et al.  Building a Large Annotated Corpus of English: The Penn Treebank , 1993, CL.

[28]  Noam Chomsky,et al.  Three models for the description of language , 1956, IRE Trans. Inf. Theory.

[29]  A. Turing.  On Computable Numbers, with an Application to the Entscheidungsproblem. , 1937 .

[30]  Jozef Kelemen,et al.  A grammar-theoretic treatment of multiagent systems , 1992 .

[31]  L. Steels.  How to do experiments in artificial language evolution and why , 2006 .

[32]  Emil L. Post.  Finite combinatory processes—formulation , 1936, Journal of Symbolic Logic.

[33]  Dana Angluin,et al.  Inference of Reversible Languages , 1982, JACM.

[34]  E. Clark,et al.  Adult reformulations of child errors as negative evidence , 2003, Journal of Child Language.

[35]  Robert Bayley,et al.  The Oxford handbook of sociolinguistics , 2013 .

[36]  Eytan Ruppin,et al.  Unsupervised learning of natural languages , 2006 .

[37]  Pedro García,et al.  IDENTIFYING REGULAR LANGUAGES IN POLYNOMIAL TIME , 1993 .

[38]  Francisco Casacuberta,et al.  Learning Finite-State Models for Machine Translation , 2004, ICGI.

[39]  Aravind K. Joshi,et al.  Natural language parsing: Tree adjoining grammars: How much context-sensitivity is required to provide reasonable structural descriptions? , 1985 .

[40]  Laurence R. Horn,et al.  The handbook of pragmatics , 2004 .

[41]  Christopher Culy,et al.  The complexity of the vocabulary of Bambara , 1985 .

[42]  Rens Bod,et al.  A DOP Model for Semantic Interpretation , 1997, ACL.

[43]  Gheorghe Paun,et al.  Grammar Systems: A Grammatical Approach to Distribution and Cooperation , 1995, ICALP.

[44]  Angelo Cangelosi.  Adaptive agent modeling of distributed language: investigations on the effects of cultural variation and internal action representations , 2007 .

[45]  Emmanuel Skordalakis,et al.  Syntactic Pattern Recognition of the ECG , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[46]  Rodney A. Brooks,et al.  Elephants don't play chess , 1990, Robotics Auton. Syst..

[47]  Aravind K. Joshi,et al.  Tree-Adjoining Grammars , 1997, Handbook of Formal Languages.

[48]  Menno van Zaanen.  ABL: Alignment-Based Learning , 2000, COLING.

[49]  Ryo Yoshinaka,et al.  Learning Mildly Context-Sensitive Languages with Multidimensional Substitutability from Positive Data , 2009, ALT.

[50]  Patrizia Grifoni,et al.  A survey of grammatical inference methods for natural language learning , 2011, Artificial Intelligence Review.

[51]  Leonor Becerra-Bonache,et al.  Iterative learning of simple external contextual languages , 2008, Theor. Comput. Sci..

[52]  Maria Dolores Jiménez-López,et al.  Modelling dialogue as inter-action , 2008, Int. J. Speech Technol..

[53]  Alexis Manaster Ramer.  Some uses and abuses of mathematics in linguistics , 1999 .

[54]  Leonor Becerra-Bonache,et al.  A Model of Semantics and Corrections in Language Learning , 2010 .