MENTAL can be used as a guestion-answering system with formatted input /output, as a vehicle for experimenting with various theories of semantic structures or as the memory management portion of a natural language question-answering system. 1. Introduction In order to develop machines capable of "understanding" natural language, it is extremely valuable, if not necessary, to design a method of organizing a corpus of data to facilitate the storage and retrieval of information on many subjects, some in depth, some in breadth; to facilitate the storage, retrieval and use of the many complex relationships among real-world concepts; to facilitate the storage, retrieval and use of information which tells how other information in the corpus may be used to further explicate implied relationships among concepts; and to facilitate the identification from the vast corpus of data of those pieces of information most directly relevant to any given topic. This paper describes a data structure (MENS) and procedures for manipulating it The research reported herein was partially supported by a grant from the National Science Foundation (GJ-583) and partially by USAF Proj.

Kuhn (1962) has persuasively argued that science progresses by means of its paradigms--its models of the general nature of a research area--and that at the frontiers of research the primary quest is for a good paradigm. The small frontier outpost of language data processing has been characterized by an intensive seeking for a paradigm suitable to guide its researchers as they survey the complex topography of natural language structures. The earliest paradigm--one that led mechanical translators and early information retrievalists into a hopeless cul-de-sac--was that words (i.e.

A question-answering system. Tech. Note 15, AI Group, Stanford Research Institute, Menlo Park, Calif.

This paper shows how a question-answering system can be constructed usingfirst-order logic as its language and a resolution-type theorem-prover as itsdeductive mechanism. A working computer-program, Q A3, based on theseideas is described. The performance of the program compares favorably withseveral other general question-answering systems.Reprinted in B. L. Webber and N. J. Nilsson (eds.), Readings in Artificial Intelligence, pp, 202-222, San Francisco: Morgan Kaufmann, 1981 Machine Intelligence 4, pp. 183-205 Meltzer, B. and Michie, D.(eds.). Edinburgh: Edinburgh University Press.

In Proc. 23rd ACM National Conference.

Simmons has presented a survey of some fifteen experimental question-answering and related systems which have been constructed since 1959. These systems take input questions in natural English (subject to varying constraints) and attempt to answer the questions on the basis of a body of information, called the data base, which is stored inside the computer. This process can be conceptually divided into three phases---syntatic analysis, semantic analysis, and retrieval, as illustrated schematically in Figure 1. The first phase consists of parsing the input sentence into a structure which explicitly represents the grammatical relationships among the words of the sentence. The remaining phase consists of procedures for either retrieving the answer directly from the data base, or else deducing the answer from information contained in the data base.

PhD. dissertation, Harvard University, August 1967. Reprinted as a volume in the series Outstanding Dissertations in the Computer Sciences, New York: Garland Publishing, 1979

This paper describes a computer system which uses a combination of coordinate indexing and structure matching techniques to extract from English questions many criteria which can be used for selecting and recognizing answers. A complete index of all content words in text is first searched to find information-rich statements which may be answers to the question. Each of these statements is then dependency analyzed to determine if the words (or synonyms) which correspond to question words maintain the dependency relations holding in the question. A simple semantic evaluation of structurally acceptable answers follows. A human editor working with the computer system helps to resolve syntactic ambiguities which are otherwise a major stumbling block in question-answering systems.

Reprinted in Marvin Minsky (ed), Semantic Information Processing, pp. 354-402, Cambridge, MA: MIT Press, 1968.

Reprinted in Marvin Minsky (ed), Semantic Information Processing, pp. 354-402, Cambridge, MA: MIT Press, 1968.