Teacher: T. S. (Tamás) Biró
Course offered by Humanities Computing (another link), RUG
Tuesdays, 13.00-15.00, room 13.13.0346.
The primary aim of the seminar is to supply students with the skills needed to carry out research (for example, in the framework of their coming MA thesis) in a field that requires computational simulations. Besides research techniques (formulating a hypothesis, running an experiment and discussing its results), oral presentation with slides and writing an academic paper also belong to these skills. Secondarily, the students will get acquainted with computational models of linguistic competence, performance, language acquisition and evolution. Guest speakers will also introduce running research at the department, so that students can choose a topic for their MA thesis.
Students are expected to be familiar with at least one programming language so that they can create extended projects themselves, without external help. Moreover, they are required to be open to abstract mathematical and computational concepts (functions, mappings, algorithms, etc.), as well as to have willingness to get acquainted with linguistic theories. No prior familiarity with linguistics or computational linguistics is expected, however.
Each student has to present at least twice: first on the theoretical background and the technical implementation of the competence and performance models (including details such as the choice of programming language and platform), and then on the results of the experiments. Each time, the student has to demonstrate the simulations running, and prepare a 20-minute-long presentation with slides.
Moreover, students have to write a paper describing the project (including technical details, motivations for the choices made, hypotheses tested, experiments and results).
The grade depends on the quality of the program (e.g., is there visualization), on the quality of the presentations (slides, presentation technique) and their content, as well as on the quality and content of the paper. The main emphasis is however on how much of the issues raised in the course are covered by the experiments: has the student experimented with all aspects of the model, have they reflected critically on the results of the experiments, discussed them, etc.?
Partha Niyogi. The Computational Nature of Language Learning and Evolution. MIT Press, 2006. (Download earlier versions: part1, part2, part3, part4, or contact me.)
New: More recommended reading