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.

Raisonnement stratifié à base de normes pour inférer les causes dans un corpus textuel

To understand texts written in natural language (LN), we use our knowledge about the norms of the domain. Norms allow to infer more implicit information from the text. This kind of information can, in general, be defeasible, but it remains useful and acceptable while the text do not contradict it explicitly. In this paper we describe a non-monotonic reasoning system based on the norms of the car crash domain. The system infers the cause of an accident from its textual description. The cause of an accident is seen as the most specific norm which has been violated. The predicates and the rules of the system are stratified: organized on layers in order to obtain an efficient reasoning.

Norm Based Causal Reasoning in Textual Corpus

Truth based entailments are not sufficient for a good comprehension of NL. In fact, it can not deduce implicit information necessary to understand a text. On the other hand, norm based entailments are able to reach this goal. This idea was behind the development of Frames (Minsky 75) and Scripts (Schank 77, Schank 79) in the 70's. But these theories are not formalized enough and their adaptation to new situations is far from being obvious. In this paper, we present a reasoning system which uses norms in a causal reasoning process in order to find the cause of an accident from a text describing it.

Why did the accident happen? A norm-based reasoning approach

In this paper we describe an architecture of a system that answer the question : Why did the accident happen? from the textual description of an accident. We present briefly the different parts of the architecture and then we describe with more detail the semantic part of the system i.e. the part in which the norm-based reasoning is performed on the explicit knowlege extracted from the text.

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.

Using Answer Set Programming in an Inference-Based approach to Natural Language Semantics

Using Answer Set Programming in an Inference-Based approach to Natural Language Semantics