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The use of augmented transition network grammars for the analysis of natural language sentences is described. Structure-building actions associated with the arcs of the grammar network allow for the reordering, restructuring, and copying of constituents necessary to produce deep-structure representations of the type normally obtained from a transformational analysis, and conditions on the arcs allow for a powerful selectivity which can rule out meaningless analyses and take advantage of semantic information to guide the parsing. The advantages of this model for natural language analysis are discussed in detail and illustrated by examples. An implementation of an experimental parsing system for transition network grammars is briefly described. [ABSTRACT FROM AUTHOR]

This paper presents a canonical form for context-sensitive derivations and a parsing algorithm which finds each context-sensitive analysis once and only once. The amount of memory required by the algorithm is essentially no more than that required to store a single complete derivation. In addition, a modified version of the basic algorithm is presented which blocks infinite analyses for grammars which contain loops. The algorithm is also compared with several previous parsers for context-sensitive grammars and general rewriting systems, and the difference between the two types of analyses is discussed. The algorithm appears to be complementary to an algorithm by S. Kuno in several respects, including the space-time trade-off and the degree of context dependence involved. [ABSTRACT FROM AUTHOR]

Recent experiments in programming natural language question-answering systems are reviewed to summarize the methods that have been developed for syntactic, semantic, and logical analysis of English strings. It is concluded that at least minimally effective techniques have been devised for answering questions from natural language subsets in small scale experimental systems and that a useful paradigm has evolved to guide research efforts in the field. Current approaches to semantic analysis and logical inference are seen to be effective beginnings but of questionable generality with respect either to subtle aspects of meaning or to applications over large subsets of English. Generalizing from current small-scale experiments to language-processing systems based on dictionaries with thousands of entries-with correspondingly large grammars and semantic systems-may entail a new order of complexity and require the invention and development of entirely different approaches to semantic analysis and question answering. [ABSTRACT FROM AUTHOR]

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 hove 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 general process for comprehending language, and it provides 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 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 language comprehension. [ABSTRACT FROM AUTHOR]

A comprehensive system for transformational grammar has been designed and implemented on the IBM 360/67 computer. The system deals with the transformational model of syntax, along the lines of Chomsky's Aspects of the Theory of Syntax. The major innovations include a full, formal description of the syntax of a transformational grammar, a directed random phrase structure generator, a lexical insertion algorithm, an extended definition of analysis, and simple problem-oriented programming language in which the algorithm for application of transformations can be expressed. In this paper we present the system as a whole, first discussing the general attitudes underlying the development of the system, then outlining the system and discussing its more important special features. References are given to papers which consider some particular aspect of the system in detail. [ABSTRACT FROM AUTHOR]

FORMAT is a production program which facilitates the editing and printing of "finished" documents directly on the printer of a relatively small (64k) computer system. It features good performance, totally free-form input, very flexible formatting capabilities including up to eight columns per page, automatic capitalization, aids for index construction, and a minimum of nontext items. It is written entirely in FORTRAN IV. [ABSTRACT FROM AUTHOR]

A further development of a computer program (ELIZA) capable of conversing in natural language is discussed. The importance of context to both human and machine understanding is stressed. It is argued that the adequacy of the level of understanding achieved in a particular conversation depends on the purpose of that conversation, and that absolute understanding on the part of either humans or machines is impossible. [ABSTRACT FROM AUTHOR]

The practical application of the theory of finite-state automata to automatically generate lexical processors is dealt with in this tutorial article by the use of the AED RWORD system, developed at M.I.T. as part of the AED-1 system. This system accepts as input descriptions of the multicharacter items or of words allowable in a language given in terms of a subset of regular expressions. The output of the system is a lexical processor which reads a string of characters and combines them into the items as defined by the regular expressions. Each output item is identified by a code number together with a pointer to a block of storage containing the characters and character count in the item. The processors produced by the system are based on finite- state machines. Each state of a "machine" corresponds to a unique condition in the lexical processing of a character string. At each state a character is read, and the machine changes to a new state. At each transition appropriate actions are taken based on the particular character read. The system has been in operation since 1966, and processors generated have compared favorably in speed to carefully hand-coded programs to accomplish the same task. Lexical processors for AED-0 and MAD are among the many which have been produced. The techniques employed are independent of the nature of the items being evaluated. If the word "events" is substituted for character string, these processors may be described as generalized decision-making mechanisms based upon an ordered sequence of events. This allows the system to be used in a range of applications outside the area of lexical processing. However convenient these advantages may be, speed is the most important consideration. In designing a system for automatic generation of a lexical processor, the goal was a processor which completely eliminated backup or rereading, which was nearly as fast as hand-coded processors, which would analyze the language and detect errors, and which would be convenient and easy to use. [ABSTRACT FROM AUTHOR]

In the first part of the paper, flow diagrams are introduced to represent inter al. mappings of a set into itself. Although not every diagram is decomposable into finite number of given base diagrams, this becomes true at a semantical level due to o suitable extension of the given set and of the basic mappings defined in it. Two normalization methods of flow diagrams are given. The first has three base diagrams; the second, only two. In the second part of the paper, the second method is applied to the theory of Turing machines. With every Turing maching provided with a two-way half-tape, there is associated similar machine, doing essentially the same job, but working on a tape obtained from the first one by interspersing alternate blank squares. The new machine belongs to the family, elsewhere introduced, generated by composition and iteration from the two machines χ and R. That family is proper subfamily of the whole family of Turing machines. [ABSTRACT FROM AUTHOR]

Summary form only given. This work describes a technique for utilizing a computer program to assign to technical papers relevant descriptors from a fixed set of such terms. The authors chose a "representative" sample of about one hundred papers from a collection of 10,000 papers previously indexed by analysts at the Defense Documentation Center. The significant content words (those not on a list of stop words to be ignored) of the title and abstract of each paper were extracted, and paired with all the descriptors for that paper. From all the pairs obtained from this teaching sample, and the relative frequency of occurrence of each descriptor, a co-occurrence value for each pair was computed, and for "validated" descriptors (those appearing at least three times in the teaching sample), this co-occurrence data was retained. The remaining descriptor names were kept on a list of "candidate" descriptors.