The conference is on grammatical inference: the field of machine learning applied to discrete combinatorial structures such as strings, trees or graphs. The conference seeks to provide a forum for presentation and discussion of original research papers on all aspects of grammatical inference including, but not limited to:
- Theoretical aspects of grammatical inference: learning paradigms, learnability results, complexity of learning.
- Efficient learning algorithms for language classes inside and outside the Chomsky hierarchy. Learning tree and graph grammars. Learning distributions over strings, trees or graphs.
- Grammatical inference from strings or trees paired with semantics representations, or learning by situated agents and robots.
- Theoretical and experimental analysis of different approaches to grammar induction, including artificial neural networks, statistical methods, symbolic methods, information-theoretic approaches, minimum description length, complexity-theoretic approaches, heuristic methods, etc.
- Novel approaches to grammatical inference: induction by DNA computing or quantum computing, evolutionary approaches, new representation spaces, etc.
- Successful applications of grammatical inference to tasks in natural language processing such as unsupervised parsing, bioinformatics, web interface design, robot navigation, machine translation, pattern recognition, language acquisition, software engineering, computational linguistics, spam and malware detection, cognitive psychology, etc.