SQA(ID:8400/)

Fischer Black, Harvard 1960
Related languages
Advice Taker => SQA   Extension of
References:
  • Black, F., "A Deductive Question-Answering System," Ph. D. Thesis, Division of Engineering and Applied Physics, Harvard University, Cambridge Mass 1964 view details
  • Simmons, R. F. "Answering English questions by computer: a survey" p53-70 view details Abstract: Fifteen experimental English language question-answering systems which are programmed and operating are described and reviewed. The systems range from a conversation machines to programs which make sentences about pictures and systems which translate from English into logical calculi. Systems are classified as list-structured data-based, graphic data-based, text-based and inferential. Principles and methods of operations are detailed and discussed.

    It is concluded that the data-base question-answerer has passed from initial research into the early developmental phase. The most difficult and important research questions for the advancement of general-purpose language processors are seen to be concerned with measuring meaning, dealing with ambiguities, translating into formal languages and searching large tree structures. DOI
          in [ACM] CACM 8(01) Jan 1965 view details
  • Black, F. "A deductive question answering system" view details
          in Minsky, Marvin (ed.), "Semantic Information Processing" Cambridge, MA: MIT Press 1968 view details
  • Schultz, Jeffrey A.; Pitts, Sarah T.; Kamery, Rob H. "From the Early 1970S: A Review Of Some Natural Language Question-Answering Systems" view details Abstract: An intelligent natural language question-answering system must be capable of performing sophisticated linguistic processing of input data in order to arrive at a well-structured internal representation upon which extensive logical processing can be performed. In our paper, the question-answering system is designed to encompass these features. A parser and a semantic converter are used to transform natural language information into the high-order language. A deduction algorithm manipulates the facts represented in the high-order language in order to synthesize the necessary information for the answering of questions posed to the system. Extract: Introduction
    Introduction
    Generally, natural language question-answering systems use some formal internal representation for facts and questions in order to facilitate deductive manipulations. In a number of earlier systems, the representation was based upon some type of limited relational calculi, i.e., Raphael’s SIR and Black’s SQA (Raphael, 1968; Black, 1968).
    Green and Raphael (1968) subsequently developed a system that offered the full expressiveness of the first-order predicate calculus for the representation of natural language information. The deductive procedure of this system was based on an automatic theorem-proving algorithm that was first described by Robinson (1965) and improved upon by Wos, Robinson, Carson, and Shalla (1967) and others. [See Green, Cordell & Raphael, 1968; Wos, Robinson & Carson, 1964a; Wos, Robinson & Carson, 1964b; Wos, Robinson & Carson, 1965; Wos, Robinson, Carson & Shalla, 1967.]
    The use of first-order predicate calculus as a formal language for the representation of natural language information when used in conjunction with automatic theorem-proving procedures was a significant improvement over previous schemes. However, the first-order predicate calculus cannot be used to express relationships between relations or allow variables to range over relations as well as objects.
    For example, suppose it is necessary to put into the first-order language the sentences:
    John crossed the street after the light changed. (1)
    or
    A car must always yield to a pedestrian. (2)
    In (1) we are unable to put the sentence into the first-order language because we have a relation, namely after, whose arguments are forced to be relations, namely crossed and changed, rather than individuals. In (2), we cannot put the sentence into the first-order language because we are faced with quantification of a variable that ranges over situations, not individuals. That is, the sentence states that for all possible situations, a certain condition holds (i.e., that a car must yield to a pedestrian).

          in Proceedings of the Academy of Information and Management Sciences, 7(2) Las Vegas, 2003 view details