Using a SMT Solver for Risk Analysis: Detecting Logical Mistakes in Texts

Author

Bannay, F. ; Lagasquie-Schiex, M.C. ; Raynaut, W. ; Saint-Dizier, P.

Author_Institution

IRIT-UPS, Toulouse, France

fYear

2014

fDate

10-12 Nov. 2014

Firstpage

867

Lastpage

874

Abstract

The purpose of this paper is to describe some results of the LELIE project, that are a contribution of Artificial Intelligence to a special domain: the analysis of the risks due to poorly written technical documents. This is a multidisciplinary contribution since it combines natural language processing with logical satisfiability checking. This paper explains how satisfiability checking can be used for detecting inconsistencies, redundancy and incompleteness in procedural texts and describes the part of the implemented tool that produces the logical translation of technical texts and realizes the checkings.

Keywords

inference mechanisms; knowledge representation; natural language processing; risk analysis; text analysis; SMT solver; artificial intelligence; knowledge representation; logical mistake detection; natural language processing; risk analysis; text analysis; Cognition; Knowledge based systems; Natural language processing; Ontologies; Redundancy; Risk analysis; AI in Natural Language Processing and Understanding; Knowledge Representation; Reasoning;

fLanguage

English

Publisher

ieee

Conference_Titel

Tools with Artificial Intelligence (ICTAI), 2014 IEEE 26th International Conference on

Conference_Location

Limassol

ISSN

1082-3409

Type

conf

DOI

10.1109/ICTAI.2014.133

Filename

6984569