Since Austin introduced the term infelicity, the linguistic literature has been flooded with its use. Today, not only performatives that fail are considered infelicitous but also utterances that are syntactically, semantically, or pragmatically ill-formed. However, no formal or computational explanation has been given for infelicity. This thesis provides one for those infelicities that occur when a pragmatic inference is cancelled. We exploit a well-known difference between pragmatic and semantic information: since implicatures and presuppositions, i.e., the carriers of pragmatic information, are not specifically uttered, pragmatic inferences are defeasible, while most of semantic inferences are indefeasible. Our contribution assumes the existence of a finer grained taxonomy with respect to pragmatic inferences. It is shown that if one wants to account for the natural language expressiveness, she should distinguish between pragmatic inferences that are felicitous to defeat and pragmatic inferences that are infelicitously defeasible. Thus, it is shown that one should consider at least three types of information: indefeasible, felicitously defeasible, and infelicitously defeasible. The cancellation of the last of these determines the pragmatic infelicities.

Abstract. During conversation, people often make assumptions or suppositions that are not explicitly stated. Failure to identify these suppositions may lead to mis-communication. In this paper, we describe a procedure that postulates such suppositions in the context of the discourse interpretation mechanism of BIAS – a Bayesian Interactive Argumentation System. When a belief mentioned in a user’s discourse differs from that obtained in BIAS ’ user model, our procedure searches for suppositions that explain this belief, preferring suppositions that depart minimally from the beliefs in the user model. Once a set of suppositions has been selected, it can be presented to the user for validation. Our procedure was evaluated by means of a web-based trial. Our results show that the assumptions posited by BIAS are considered sensible by our trial subjects. 1

Lexical knowledge is increasingly important in language engineering. However, it is a difficult kind of knowledge to represent and reason with. Existing approaches to formalizing lexical knowledge have used languages with limited expressibility and in particular they have not addressed the context-dependent nature of lexical knowledge. Here we present a framework, based on default logic, called the dex framework, for capturing context-dependent reasoning with lexical knowledge. Default logic is a first-order logic offering a more expressive formalisation than inheritance hierarchies: (1) First-order formulae about words can be inferred; (2) Preferences over formulae can be based on specificity, reasoning about exceptions, or explicit priorities; (3) Contexts can be reasoned with as first formulae; and (4) Contexts can be derived as default inferences. In the dex framework, the word for which further information is sought called the query word. The context for a query word is derived fr...

Many parsers take natural langauge as input, and give some form of parse, in some formal language, as output. However, most parsers use little or no world knowledge to check the commonsense of the output or to facilitate inferencing from the output. Here we look at how world knowledge can be used for commonsense checking and commonsense inferencing with output from parsing. In the framework that we present in this paper, we assume that the output of the parser is in the form of a predicate-argument structure. We encode this output of parsing in classical first-order logic. Commonsense checking involves classical consistency checking of the output with world knowledge whereas commonsense inferencing involves classical thereom proving with world knowledge. The encodation is based on treating each symbol used in the language of the predicate-argument structures as a classical n-ary function symbol, where n 0. The encodation also assumes a set of predicate symbols for capturing relationships between predicate-argument structures output by parsers and for capturing world knowledge.

Lexical knowledge is increasingly important in information systems --- for example in indexing documents using keywords, or disambiguating words in a query to an information retrieval system, or a natural language interface. However, it is a difficult kind of knowledge to represent and reason with. Existing approaches to formalizing lexical knowledge have used languages with limited expressibility, such as those based on inheritance hierarchies, and in particular, they have not adequately addressed the context-dependent nature of lexical knowledge. Here we present a framework, based on default logic, called the dex framework, for capturing context-dependent reasoning with lexical knowledge. Default logic is a firstorder logic offering a more expressive formalisation than inheritance hierarchies: (1) Firstorder formulae capturing lexical knowledge about words can be inferred; (2) Preferences over formulae can be based on specificity, reasoning about exceptions, or explicit pri...