Information Extraction and Knowledge Acquisition from Texts Using Bilingual Question–Answering

  • Authors:

  • John Kontos;Ioanna Malagardi

  • Affiliations:

  • Department of Informatics, Athens University of Economics and Business, 76 Patission St., 104 34 Athens, Greece/ e-mail: jpk@aueb.gr;Department of Informatics, Athens University of Economics and Business, 76 Patission St., 104 34 Athens, Greece/ e-mail: jpk@aueb.gr

  • Venue:
  • Journal of Intelligent and Robotic Systems
  • Year:
  • 1999

Quantified Score

Hi-index 0.00

Visualization

Abstract

A novel approach is introduced in this paper for the implementation of a question–answering based tool for the extraction of information and knowledge from texts. This effort resulted in the computer implementation of a system answering bilingual questions directly from a text using Natural Language Processing. The system uses domain knowledge concerning categories of actions and implicit semantic relations. The present state of the art in information extraction is based on the template approach which relies on a predefined user model. The model guides the extraction of information and the instantiation of a template that is similar to a frame or set of attribute value pairs as the result of the extraction process. Our question–answering based approach aims to create flexible information extraction tools accepting natural language questions and generating answers that contain information extracted from text either directly or after applying deductive inference. Our approach also addresses the problem of implicit semantic relations occurring either in the questions or in the texts from which information is extracted. These relations are made explicit with the use of domain knowledge. Examples of application of our methods are presented in this paper concerning four domains of quite different nature. These domains are: oceanography, medical physiology, aspirin pharmacology and ancient Greek law. Questions are expressed both in Greek and English. Another important point of our method is to process text directly avoiding any kind of formal representation when inference is required for the extraction of facts not mentioned explicitly in the text. This idea of using text as knowledge base was first presented in Kontos [7] and further elaborated in [9,11,12] as the ARISTA method. This is a new method for knowledge acquisition from texts that is based on using natural language itself for knowledge representation.