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Toward Natural Language Computation '

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The ability how they can be combined. Thus the user would be to program in natural language instead of traditional taxed more heavily with a natural language system programming languages would enable people to use than with a traditional system. A second argument familiar constructs in expressing their requests, thus against natural language programming relates to its making machines accessible to a wider user group.


Concerning 3), we are interested in being able to predict

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It is shown that the algorithm has robust performance for a wide variety of inputs and that it converges to a solution on the basis some LISP programs can be generated from just inputoutput of minimum input information.


ON THE SYNTHESIS OF FINITE-STATE MACHINES FROM SAMPLES OF THEIR BEHAVIOR

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Techniques have been given for machine synthesis from input-output behavior by Gill [7], Ginsburg [8], [9], Gray Nerode [15] has given a method for synthesizing finitestate and Harrison [11], Tal [17], and others. Each of these machines from their associated right-invariant equivalence methods requires that enough information be included in relations. In this note, we introduce a modification of the problem statement so that the solution is unique, and the Nerode relation and show how it can be used to synthesize in contrast to the method presented here, they do not have a machines from finite subsets of their behavior. The capability to utilize unspecified or DON'T CARE conditions to technique described is a method for finding a nondeterministic produce simpler solutions. The method described here yields machine that realizes a given finite set of input - machines that satisfy the known input-output requirements output pairs, and it includes a parameter k that allows one and that often given "reasonable" behavior outside of the to vary the precision and complexity of the synthesized machine.



On the Inference of Turing Machines from Sample Computations A. W. Biermann

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This paper will be concerned with the problem of obtaining this performance from the machine by giving it examples of the desired computation and having it program itself. We will be concerned with designing a trainable Turing machine although the concepts presented are applicable in a much more general context as discussed in Section 4. The Turing machine to be discussed here will have an infinite one dimensional tape and will have the capability in one move to read a symbol on the tape, print a new symbol to replace the one just read, and step right or left one increment on the tape. It will have a deterministic finite-state controller with a designated initial state which will upon receiving an input symbol read from the tape, yield the symbol to be printed and the step direction (right or left) to be made.


AUTOMATA STUDIES

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Printed in the United States of America PREFACE Among the most challenging scientific questions of our time are the corresponding analytic and synthetic problems: How does the brain function? Can we design a machine which will simulate a brain? Speculation on these problems, which can be traced back many centuries, usually reflects in any period the characteristics of machines then in use. Descartes, in DeBomine, sees the lower animals and, in many of his functions, man as automata. Using analogies drawn from water-clocks, fountains and mechanical devices common to the seventeenth century, he imagined that the nerves transmitted signals by tiny mechanical motions.


22 Question Answering BONNIE WEBBER AND NICK WEBB

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Questions are asked and answered every day. Question answering (QA) technology aims to deliver the same facility online. It goes further than the more familiar search based on keywords (as in Google, Yahoo, and other search engines), in attempting to recognize what a question expresses and to respond with an actual answer. First, questions do not often translate into a simple list of keywords. For example, the question (1) Which countries did the pope visit in the 1960s? A much more complex set of keywords is needed in order to get anywhere close to the intended result, and experience shows that people will not learn how to formulate and use such sets. Second, QA takes responsibility for providing answers, rather than a searchable list of links to potentially relevant documents (web pages), highlighted by snippets of text that show how the query matched the documents. While this is not much of a burden when the answer appears in a snippet and further document access is unnecessary, QA technology aims to move this from being an accidental property of search to its focus. In keyword search and in much work to date on QA technology, the information seeking process has been seen as a one-shot affair: the user asks a question, and the system provides a satisfactory response. However, early work on QA (Section 1.1) did not make this assumption, and newly targeted applications are hindered by it: while a user may try to formulate a question whose answer is the information Question Answering 631 they want, they will not know whether they have succeeded until something has been returned for examination. If what is returned is unsatisfactory or, while not the answer, is still of interest, a user needs to be able to ask further questions that are understood in the context of the previous ones. For these target applications, QA must be part of a collaborative search process (Section 3.3). In the rest of this section, we give some historical background on QA systems (Section 1.1), on dialogue systems in which QA has played a significant role (Section 1.2), and on a particular QA task that has been a major driver of the field over the past 8 years (Section 1.3). Section 2 describes the current state of the art in QA systems, organized around the de facto architecture of such systems. Section 3 discusses some current directions in which QA is moving, including the development of interactive QA.