Une expérience de sémantique inférentielle

We develop a system which must be able to perform the same inferences that a human reader of an accident report can do and more particularly to determine the apparent causes of the accident. We describe the general framework in which we are situated, linguistic and semantic levels of the analysis and the inference rules used by the system.

The role of time in considering collections

The paper concerns the understanding of plurals in the framework of Artificial Intelligence and emphasizes the role of time. The construction of collection(s) and their evolution across time is often crucial and has to be accounted for. The paper contrasts a "de dicto" collection where the collection can be considered as persisting over these situations even if its members change with a "de re" collection whose composition does not vary through time. It expresses different criteria of choice between the two interpretations (de re and de dicto) depending on the context of enunciation.

Challenging the principle of compositionality in interpreting natural language texts

The paper aims at emphasizing that, even relaxed, the hypothesis of compositionality has to face many problems when used for interpreting natural language texts. Rather than fixing these problems within the compositional framework, we believe that a more radical change is necessary, and propose another approach.

Representing Knowledge about Norms

Norms are essential to extend inference: inferences based on norms are far richer than those based on logical implications. In the recent decades, much effort has been devoted to reason on a domain, once its norms are represented. How to extract and express those norms has received far less attention. Extraction is difficult: as the readers are supposed to know them, the norms of a domain are seldom made explicit. For one thing, extracting norms requires a language to represent them, and this is the topic of this paper. We apply this language to represent norms in the domain of driving, and show that it is adequate to reason on the causes of accidents, as described by car-crash reports.

About Norms and Causes

Knowing the norms of a domain is crucial, but there exist no repository of norms. We propose a method to extract them from texts: texts generally do not describe a norm, but rather how a state-of-affairs differs from it. Answers concerning the cause of the state-of-affairs described often reveal the implicit norm. We apply this idea to the domain of driving, and validate it by designing algorithms that identify, in a text, the "basic" norms to which it refers implicitly.