Abstract not found

One way to solve the knowledge acquisition bottleneck is to have ways to translate natural language sentences and discourses to a formal knowledge representation language, especially ones that are appropriate to express domain knowledge in sciences, such as Biology. While there have been several proposals, including by Montague (1970), to give model theoretic semantics for natural language and to translate natural language sentences and discourses to classical logic, none of these approaches use knowledge representation languages that can express domain knowledge involving normative statements and exceptions. In this paper we take a first step to illustrate how one can automatically translate natural language sentences about normative statements and exceptions to representations in the knowledge representation language Answer Set Programming (ASP). To do this, we use λ-calculus representation of words and their composition as dictated by a CCG grammar.

This paper argues that all subject noun phrases can be given a quantified formalisation in terms of the intersection between their denotation set and the denotation set of their verbal predicate. The majority of subject noun phrases, however, are only implicitely quantified and the task of retrieving the most plausible quantifier for a given NP is non-trivial. We propose a formalisation which captures the underspecification of the quantifier in subject NPs and we show that this formalisation is widely applicable, including in statements involving kinds. We then present a baseline for a quantification resolution system using syntactic features as basis for classification. Although the syntactic baseline provides a respectable 78 % precision, our error analysis shows that obtaining true performance on the task requires information beyond syntax. 1 Quantification resolution Most subject noun phrases in English are not explicitly quantified. Still, humans are able to give them quantificational interpretations in context: 1. Cats are mammals = All cats... 2. Cats have four legs = Most cats...