The workshop is devoted to psychologically-motivated computational models of language acquisition. That is, models which are compatible with research in psycholinguistics, developmental psychology and linguistics.
This is the sixth meeting of the Psychocomputational Models of Human Language Acquisition workshop following PsychoCompLA-2004, held in Geneva, Switzerland as part of the 20th International Conference on Computational Linguistics (COLING- 2004), PsychoCompLA-2005 as part of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL-2005) held in Ann Arbor, Michigan where the workshop shared a joint session with the Ninth Conference on Computational Natural Language Learning (CoNLL-2005), PsychoCompLA-2007 held in Nashville, Tennessee as part of the 29th meeting of the Cognitive Science Society (CogSci- 2007), PsychoCompLA-2008 held in Washington D.C., as part of the 30th meeting of the Cognitive Science Society (CogSci-2008), and PsychoCompLA-2009 held over two days before the 31st meeting of the Cognitive Science Society (CogSci-2009) in Amsterdam, Netherlands.
The workshop will present research and foster discussion centered around psychologically-motivated computational models of language acquisition, with an emphasis on the acquisition of syntax. In recent decades there has been a thriving research agenda that applies computational learning techniques to emerging natural language technologies and many meetings, conferences and workshops in which to present such research. However, there have been only a few (but growing number of) venues in which psychocomputational models of how humans acquire their native language(s) are the primary focus. Psychocomputational models of language acquisition are of particular interest in light of recent results in developmental psychology that suggest that very young infants are adept at detecting statistical patterns in an audible input stream. However, how children might plausibly apply statistical 'machinery' to the task of grammar acquisition, with or without an innate language component, remains an open and important question. One effective line of investigation is to computationally model the acquisition process and determine interrelationships between a model and linguistic or psycholinguistic theory, and/or correlations between a model's performance and data from linguistic environments that children are exposed to.
Topics and Goals:
Given the collocation of the workshop with the Input and Syntactic Acquisition workshop, submissions that present research related to the acquisition of syntax are strongly encouraged, though submissions on the computational modelling on any aspect of human language acquisition are welcome.
Specifically, submissions on (but not necessarily limited to) the following topics are welcome:
· Models that address the acquisition of word-order;
· Models that combine parsing and learning;
· Formal learning-theoretic and grammar induction models that incorporate psychologically plausible constraints;
· Comparative surveys that critique previously reported studies;
· Models that have a cross-linguistic or bilingual perspective;
· Models that address learning bias in terms of innate linguistic knowledge versus statistical regularity in the input;
· Models that employ language modeling techniques from corpus linguistics;
· Models that employ techniques from machine learning;
· Models of language change and its effect on language acquisition or vice versa;
· Models that employ statistical/probabilistic grammars;
· Computational models that can be used to evaluate existing linguistic or developmental theories (e.g., principles & parameters, optimality theory, construction grammar, etc.)
· Empirical models that make use of child-directed corpora such as CHILDES.
This workshop intends to bring together researchers from cognitive psychology, computational linguistics, other computer/mathematical sciences, linguistics and psycholinguistics working on all areas of language acquisition. Diversity and cross-fertilization of ideas is the central goal.