Topics of interest
- Theoretical aspects of grammatical inference: learning paradigms, learnability results, complexity of learning.
- Empirical and theoretical research on query learning, active learning, and other interactive learning paradigm
- Empirical and theoretical research on methods using or including, but not limited to, spectral learning, state-merging, distributional learning, statistical relational learning, statistical inference and/or Bayesian learning
- Learning algorithms for language classes inside and outside the Chomsky hierarchy. Learning tree and graph grammars.
- Learning probability distributions over strings, trees or graphs, or transductions thereof.
- Learning with contextualized data: for instance, Grammatical inference from strings or trees paired with semantics representations, or learning by situated agents and robots.
- Experimental and theoretical 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 learning to tasks in fields including, but not limited to, natural language processing and computational linguistics, model checking and software verification, bioinformatics, robotic planning and control, and pattern recognition.