Logical and Computational Structures for Linguistic Modeling
From the webpage:
Computational linguistics employs mathematical models to represent morphological, syntactic, and semantic structures in natural languages. The course introduces several such models while insisting on their underlying logical structure and algorithmics. Quite often these models will be related to mathematical objects studied in other MPRI courses, for which this course provides an original set of applications and problems.
The course is not a substitute for a full cursus in computational linguistics; it rather aims at providing students with a rigorous formal background in the spirit of MPRI. Most of the emphasis is put on the symbolic treatment of words, sentences, and discourse. Several fields within computational linguistics are not covered, prominently speech processing and pragmatics. Machine learning techniques are only very sparsely treated; for instance we focus on the mathematical objects obtained through statistical and corpus-based methods (i.e. weighted automata and grammars) and the associated algorithms, rather than on automated learning techniques (which is the subject of course 1.30).
Abundant supplemental materials, slides, notes, further references.
In particular you may like Notes on Computational Aspects of Syntax by Sylvain Schmitz, that cover the first part of Logical and Computational Structures for Linguistic Modeling.
As with any model, there are trade-offs and assumptions build into nearly every choice.
Knowing where to look for those trade-offs and assumptions will give you a response to: “Well, but the model shows that….”