From the textual description of an accident to its causes

  • Authors:

  • Daniel Kayser;Farid Nouioua

  • Affiliations:

  • Laboratoire d'Informatique de Paris-Nord, UMR 7030 du C.N.R.S. -- Institut Galilée, Université Paris 13, 99 avenue Jean-Baptiste Clément, F 93430 -- Villetaneuse, France;Laboratoire d'Informatique de Paris-Nord, UMR 7030 du C.N.R.S. -- Institut Galilée, Université Paris 13, 99 avenue Jean-Baptiste Clément, F 93430 -- Villetaneuse, France

  • Venue:
  • Artificial Intelligence
  • Year:
  • 2009

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Abstract

Every human being, reading a short report concerning a road accident, gets an idea of its causes. The work reported here attempts to enable a computer to do the same, i.e. to determine the causes of an event from a textual description of it. It relies heavily on the notion of norm for two reasons:*The notion of cause has often been debated but remains poorly understood: we postulate that what people tend to take as the cause of an abnormal event, like an accident, is the fact that a specific norm has been violated. *Natural Language Processing has given a prominent place to deduction, and for what concerns Semantics, to truth-based inference. However, norm-based inference is a much more powerful technique to get the conclusions that human readers derive from a text. The paper describes a complete chain of treatments, from the text to the determination of the cause. The focus is set on what is called ''linguistic'' and ''semantico-pragmatic'' reasoning. The former extracts so-called ''semantic literals'' from the result of the parse, and the latter reduces the description of the accident to a small number of ''kernel literals'' which are sufficient to determine its cause. Both of them use a non-monotonic reasoning system, viz. LPARSE and SMODELS. Several issues concerning the representation of modalities and time are discussed and illustrated by examples taken from a corpus of reports obtained from an insurance company.