This book pursues the recent upsurge of research in the interface of logic, language and computation, with applications 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. The papers in this volume cover: 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.
Preface; 1. Indexicals, contexts, and unarticulated constituents; 2. Formalizing context (expanded notes); 3. Changing contexts and shifting assertions; 4. Discourse preferences in dynamic logic; 5. Polarity, predicates and monotonicity; 6. Machine learning of physics word problems.
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