Abstract
Fluid Construction Grammars (FCGs) are a flavor of Construction Grammars, which are themselves unification-based grammars.
The FCG syntax is similar to that of other unification-based grammars only to a small extent. Additionally, up until now, FCG has lacked a comprehensively-defined declarative semantics, whereas its procedural semantics is truly particular compared to other unification-based grammar formalisms.
Here we propose the re-definition of a core subset of the FCG formalism (henceforth called FCG light) within the framework of order-sorted feature constraint logics (OSF-logic) that would assign FCG a rigorous semantics, both declarative and procedural, that is suitable for both parsing, production and grammar learning.
This new framework allows us to clearly compare FCG to other unification-based grammars. We will also have the advantage of associating FCG with another classical paradigm for learning (“evolving”) new grammars, namely learning in hierarchies (lattices) of concepts. This learning technique exploits the natural partial order relation of generalization/ specialization between grammars. The learning method currently used by FCG, is (inspired by) reinforcement learning. We claim that learning in a hierarchy of grammar versions enables us to establish a rather natural link with linguistic background knowledge when devising the grammar repair strategies. It also sets a stage on which we may compare different grammars that could be learned by an agent at each step during the grammar evolution process.
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Ciortuz, L., Saveluc, V. (2012). Fluid Construction Grammar and Feature Constraint Logics. In: Steels, L. (eds) Computational Issues in Fluid Construction Grammar. Lecture Notes in Computer Science(), vol 7249. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34120-5_12
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DOI: https://doi.org/10.1007/978-3-642-34120-5_12
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