This book pursues the recent upsurge of research along the interface of logic, language and computation, adding connections to artificial intelligence and machine learning. It contains a variety of contributions to the logical and computational analysis of natural language. A wide range of logical and computational tools are employed, and applied to such varied areas as context-dependency, linguistic discourse, and formal grammar.
In more detail, the papers in this volume deal with context-dependency from philosophical, computational, and logical points of view; a logical framework for combining dynamic discourse semantics and preferential reasoning in AI; negative polarity items in connection with affective predicates; Head-Driven Phrase Structure Grammar from a perspective of type theory and category theory; and an axiomatic theory of machine learning of natural language, with applications to physics word problems.
is Associate Professor at the Institute for Philosophical Research, National Autonomous University of Mexico (UNAM) in Mexico City, Mexico. is a research associate at the Compute Science Department of Stanford University. is Associate Professor of Philosophy at the Department of Philosophy, Stockholm University, Sweden.
- 1 Indexicals, Contexts and Unarticulated Constituents
- 2 Formalizing Contexts (Expanded Notes)
- 3 Changing Contexts and Shifting Assertations
- 4 Discourse Preferences in Dynamic Logic
- 5 Polarity, Predicates and Monotonicity
- 6 HPSG as Type Theory
- 7 Machine Learning of Physics Word Problems