5,937 research outputs found
Learning unification-based grammars using the Spoken English Corpus
This paper describes a grammar learning system that combines model-based and
data-driven learning within a single framework. Our results from learning
grammars using the Spoken English Corpus (SEC) suggest that combined
model-based and data-driven learning can produce a more plausible grammar than
is the case when using either learning style isolation.Comment: 10 page
E-Generalization Using Grammars
We extend the notion of anti-unification to cover equational theories and
present a method based on regular tree grammars to compute a finite
representation of E-generalization sets. We present a framework to combine
Inductive Logic Programming and E-generalization that includes an extension of
Plotkin's lgg theorem to the equational case. We demonstrate the potential
power of E-generalization by three example applications: computation of
suggestions for auxiliary lemmas in equational inductive proofs, computation of
construction laws for given term sequences, and learning of screen editor
command sequences.Comment: 49 pages, 16 figures, author address given in header is meanwhile
outdated, full version of an article in the "Artificial Intelligence
Journal", appeared as technical report in 2003. An open-source C
implementation and some examples are found at the Ancillary file
Learning morphological phenomena of Modern Greek an exploratory approach
This paper presents a computational model for the description of concatenative morphological phenomena of modern Greek (such as inflection, derivation and compounding) to allow learners, trainers and developers to explore linguistic processes through their own constructions in an interactive openāended multimedia environment. The proposed model introduces a new language metaphor, the āpuzzleāmetaphorā (similar to the existing āturtleāmetaphorā for concepts from mathematics and physics), based on a visualized unificationālike mechanism for pattern matching. The computational implementation of the model can be used for creating environments for learning through design and learning by teaching
Semantic Unification A sheaf theoretic approach to natural language
Language is contextual and sheaf theory provides a high level mathematical
framework to model contextuality. We show how sheaf theory can model the
contextual nature of natural language and how gluing can be used to provide a
global semantics for a discourse by putting together the local logical
semantics of each sentence within the discourse. We introduce a presheaf
structure corresponding to a basic form of Discourse Representation Structures.
Within this setting, we formulate a notion of semantic unification --- gluing
meanings of parts of a discourse into a coherent whole --- as a form of
sheaf-theoretic gluing. We illustrate this idea with a number of examples where
it can used to represent resolutions of anaphoric references. We also discuss
multivalued gluing, described using a distributions functor, which can be used
to represent situations where multiple gluings are possible, and where we may
need to rank them using quantitative measures.
Dedicated to Jim Lambek on the occasion of his 90th birthday.Comment: 12 page
- ā¦