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The teachable language comprehender: a simulation program and theory of language

Author:

M. Ross QuillianAuthors Info & Claims

Communications of the ACM, Volume 12, Issue 8

Pages 459 - 476

https://doi.org/10.1145/363196.363214

Published: 01 August 1969 Publication History

Abstract

The Teachable Language Comprehender (TLC) is a program designed to be capable of being taught to “comprehend” English text. When text which the program has not seen before is input to it, it comprehends that text by correctly relating each (explicit or implicit) assertion of the new text to a large memory. This memory is a “semantic network” representing factual assertions about the world.

The program also creates copies of the parts of its memory which have been found to relate to the new text, adapting and combining these copies to represent the meaning of the new text. By this means, the meaning of all text the program successfully comprehends is encoded into the same format as that of the memory. In this form it can be added into the memory.

Both factual assertions for the memory and the capabilities for correctly relating text to the memory's prior content are to be taught to the program as they are needed. TLC presently contains a relatively small number of examples of such assertions and capabilities, but within the system, notations for expressing either of these are provided. Thus the program now corresponds to a general process for comprehending language, and it provides a methodology for adding the additional information this process requires to actually comprehend text of any particular kind.

The memory structure and comprehension process of TLC allow new factual assertions and capabilities for relating text to such stored assertions to generalize automatically. That is, once such an assertion or capability is put into the system, it becomes available to help comprehend a great many other sentences in the future. Thus the addition of a single factual assertion or linguistic capability will often provide a large increment in TLC's effective knowledge of the world and in its overall ability to comprehend text.

The program's strategy is presented as a general theory of

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Cited By

Millsap RMeredith W(2025)Structure in Semantic Memory: A Probabilistic Approach using a Continuous Response TaskPsychometrika10.1007/BF0229395452:1(19-41)Online publication date: 1-Jan-2025
https://doi.org/10.1007/BF02293954
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https://doi.org/10.1177/14788047241277768
Kiefer M(2024)Verkörperte BegriffeNervenheilkunde10.1055/a-2313-360843:09(499-505)Online publication date: 27-Aug-2024
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Index Terms

The teachable language comprehender: a simulation program and theory of language
1. Applied computing
  1. Arts and humanities
    1. Language translation
2. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
  2. Modeling and simulation
    1. Model development and analysis
      1. Model verification and validation

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Published In

cover image Communications of the ACM

Communications of the ACM Volume 12, Issue 8

Aug. 1969

59 pages

ISSN:0001-0782

EISSN:1557-7317

DOI:10.1145/363196

Editor:
M. Stuart Lynn
IBM Scientific Center, Houston, TX

Issue’s Table of Contents

Copyright © 1969 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 August 1969

Published in CACM Volume 12, Issue 8

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Millsap RMeredith W(2025)Structure in Semantic Memory: A Probabilistic Approach using a Continuous Response TaskPsychometrika10.1007/BF0229395452:1(19-41)Online publication date: 1-Jan-2025
https://doi.org/10.1007/BF02293954
Malik AFürstenau B(2024)Representational visuals of abstract financial concepts: A means to foster financial literacyCitizenship, Social and Economics Education10.1177/1478804724127776823:3(144-164)Online publication date: 9-Sep-2024
https://doi.org/10.1177/14788047241277768
Kiefer M(2024)Verkörperte BegriffeNervenheilkunde10.1055/a-2313-360843:09(499-505)Online publication date: 27-Aug-2024
https://doi.org/10.1055/a-2313-3608
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Badie FAugusto L(2023)Fuzzy Networks for Modeling Shared Semantic KnowledgeJournal of Artificial General Intelligence10.2478/jagi-2023-000114:1(1-14)Online publication date: 27-Mar-2023
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Budel GJin YVan Mieghem PKitsak M(2023)Topological properties and organizing principles of semantic networksScientific Reports10.1038/s41598-023-37294-813:1Online publication date: 20-Jul-2023
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