Simulating human language understanding on the computer is a great
challenge. A way to approach it is to represent natural language meanings
in logic, and to use logical provers to determine what does and does not
follow from a text. What logic is best to use and how natural language
meanings are best represented in it are far from trivial questions.
This thesis focuses on semantic representation in deep parsing. It
describes the Delilah parser and generator for Dutch, which computes
semantic representations for sentences, discussing several issues and
proposing some further improvements to the system. A style of logical form
is developed that is optimized for inference in mainly two ways. One is the
implementation of event semantics for verbs and nominalizations and with
underlying states for intersective adjectives and their corresponding
abstract nouns. This makes many entailments follow straightforwardly. The
second is the introduction of Flat Logical Form, as an alternative to
first-order logic representations. In Flat Logical Form, crucial
information on quantification, monotonicity, and embedding is annotated
locally on the variables of the formula, where it does not complicate the
formula's structure. Bothmovesmake the representations rich in information
and at the same time easy to process for purposes of automated reasoning.
Such automated reasoning with access to detailed semantic information is
expected to contribute to the retrieval of free narrative text.
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