Search

This paper describes the development and application of an algorithm which generates non-linear representations of English text. The algorithm uses the results of a syntactic analysis system and a set of rules which prescribe linkages to generate a graph of a sentence. The shape of these graphs corresponds to the syntax of the sentence; the labels correspond to the vocabulary of the sentence and the edge types correspond to case grammar roles. The sentence graphs can then be interconnected at common nodes and analyzed according to common edges. Preliminary experimentation has yielded promising results. It appears that the algorithm produces a representation of English text which could be quite useful in automatic language processing.

SUMMARY Examination of the criteria employed by ALLEN (1963) in his classification of cross-stratified units suggests (a) that an additional criterion, the three-dimensional shape of cross-strata, should be used in describing and recognizing varieties of cross-stratification, (b) that the character of the lower bounding surface of sets should be expressed in descriptive rather than genetic terms, and (c) that in describing the relation between cross-strata and lower bounding surfaces, an additional term, asymptotic, should be used. The recent discussion of the morphological features of cross-stratification by ALLEN (1963) represents a significant advance in our appreciation of the variability of this type of sedimentary structure. The valuable classification of McKEE and WEIR (1953) generated a concern for precise description of cross-stratification which has, in 10 years, made the classification inadequate. Allen's proposed classification, based as it is on observable morphological criteria and utilising a nongenetic terminology, should promote further rapid progress. Allen has discussed the problems that arise in naming varieties of cross-stratification. He very properly avoids terms with genetic implications and his use of Greek letters results in commendably brief names. Almost inevitably, his chosen nomenclature is not readily applicable in the field due to the difficulty of remembering which letter is associated with which assemblage of morphological features. This difficulty will increase as the number of defined varieties increases. It seems probable that workers will prefer for the most part to give names to varieties of cross-stratification only in the laboratory where reference to literature can be made. The basis for recognition of varieties will be detailed field notes and photographs of morphological features. Nor is this undesirable, for it necessitates the making of detailed observations in the field which will lead to the recognition of new varieties. An easily remembered nomenclature too often leads to the suppression of significant differences in the interests of “pigeon-holing” the structure in question. It is therefore important that all significant morphological features should be noted. Consideration of the differences between certain varieties of cross-stratification.

Items have been studied heretofore for their value as elements of particular tests to the neglect of more fundamental research into the multiple potentiality of items. This article proposes a method of grouping items into “synonymies” comprising all of the items which correlate with a given key item. These synonymies can be used for interpretation of the total meaning of the key item: (1) by inspection of the constituent items and (2) by correlational study of obtained single scores of individual persons. The method is illustrated by four items with inter- and intra-correlations, and characteristics of an ideal background reservoir of items are pointed out.

THE recent communication on this subject by R. Robinson1 has prompted me to record some observations made about two years ago on this subject.

Publisher Summary This chapter focuses on connection theory of children's language and linguistic theory. It discusses the problem of a child's recognition of nonuniversal aspects of deep structure. It is not the case that deep structure is the same for all languages. Grammatical relations are, by hypothesis, universal, but the order of elements is not. It is not the case that simple, active, declarative sentences always reflect deep structure order directly. It is not the case that the most frequent forms in surface structure reflect deep structure. Traditionally, the determination of base word order depends upon analysis of the transformational component. The most important transformation in German is the verb-second transformation which transfers an element from final position to second position in the creation of declarative sentences. It is the broad applicability of this transformation that makes elegantly simple a description of German that uses a verb-final base structure. The subordinate-clause strategy is a powerful mechanism for the discovery of deep structure.

The report is a collection of Working Papers in Speech Recognition on the following topics: Organization of the HEARSAY II speech understanding system; The DRAGON system -- an overview; Parameter-independent machine segmentation and labeling; A new time-domain analysis of fricatives and stop consonants; Sub-lexical levels in the HEARSAY II speech understanding system; Inference and use of simple predictive grammars; Real-time linear predictive coding of speech on the SPS-41 microprogrammed triple-processor system; A 16-bit A-D-A conversion system for high-fidelity audio research.

This paper as I understand it tries to set up four levels of linguistic representation in accordance with semantic interpretation. I shall go directly to p. 424 where an outline is given as Table I.

As part of a research program aimed at determining the parameters influencing the effectiveness of a message retrieval system, a collection of 10, 000 technical abstracts was indexed and retrieval experiments were conducted with them. Since part of the work involved the development and test operation of an associative retrieval system, basic data about the distribution of words and word strings were gathered in preparing the system for test and trial. These statistics were thought to be of possible interest to other workers in the field and are gathered as a series of loosely connected papers in this volume under the following groupings: Characteristics and Indexing of GE Data Base; Comparison of Manual and Machine Selected Vocabularies; Vocabulary Distribution Studies, and Studies of Content Bearing Units in Text.

The standard method of identifying a typewriter as having typed a certain document has been to examine the typed impressions on the document for irregularities or identifying characteristics peculiar to that certain typewriter, as distinguished from all other typewriters of the same class. Among the identifying individualities considered are alignment, characters striking off their feet (striking harder on one side of the character than on the other), slant, and damage to the type face. Positive identification requires a sufficient amount of questioned and exemplar type to eliminate the element of chance and establish a definite pattern. In practice, this has not always been easily accomplished. Frequently a very limited amount of typing is available, and additional difficulty arises when the typewriter in question is fairly new and individual identifying characteristics are few.

This paper describes a system of computer-aided diagnosis using an English Electric KDF9 computer linked to a terminal in a busy clinical department. Data from a series of patients were recorded, coded, and entered into the computer, which then performed a Bayesian analysis and displayed diagnostic probabilities in an adaptable format. Experience in this setting suggests that computer diagnosis may be a valuable aid to the clinician.

著者はブドウ酒酵1づ:の支芽汁培jl.S液から昼光を避けた状態で,cthcrを用いて脚臭を脱臭する性質を有する結晶性の物質を得た。本物質はpapcrchro一matOgralnによるRf値,昇華性,P01ymOrPhiSnl)融点,溶解度,アルカリ摘定,元素分析,紫外線吸収,赤外線吸収,旋光性,X線回折などによって検討した結果,元素分析゛値,赤外線吸収は既知のコハク酸と酷似しており,他の点では異なった性状が認められた。そこで本物質はコハク酸の立体巽性体(cis21;慧およびcis型に近いCauchc型)であろうと推定した。

String analysis has developed out of an attempt to carry out syntactic analysis on a computer, just as, some ten years earlier, transformational analysis developed out of the attempt to normalize texts for discourse analysis. The arrangement of syntax for computability, following in part the method presented in ‘From Morpheme to Utterance’ (Language 22 (1946), 161–83; Paper VI of this volume) was based on an effective procedure for finding in each sentence a sequence (in general, broken) of words which was itself a sentence, belonging to a certain set of minimal sentence structures. This minimal sentence was called the center of the given sentence, and its meaning had an important and central relation to the meaning of the given sentence; this relation can be specified independently of the given sentence. The remainder of the sentence consisted of adjunctions to the center or to the adjunctions; an effective procedure was presented for an ordered determining of these adjunctions, and the ordered adjunctions had an interpretation independent of the given sentence. The original version of this analysis, made for the Univac sentence-decomposing program of 1959, is given in Computable Syntactic Analysis (TDAP 15), 1959 (Paper XVI of this volume).1

Curiously, the ACM First Glossary of Programming Terminology has no entry for the word "document." The American College Dictionary defines it as "a written or printed paper furnishing information or evidence, a legal or official paper." Without becoming lexicographers, we might observe that "write" means "to trace or form (characters, letters, words, etc.) on the surface of some material, as with a pen, pencil or other instrument or means; inscribe."

The purpose of this paper is to define a mathematical model for the study of quantitative problems about translations between universal languages and to investigate such problems. The results derived in this paper deal with the efficiency of the translated algorithms, the optimality of translations and the complexity of the translation process between different languages.

This paper presents the results of research into the development of a semantic-based picture recognition technique. The need for such a technique is reflected in the limitations of syntactic picture recognition techniques, which are discussed in detail. The motivation for developing a semantic-based technique arises from a few intuitive notions described in the paper. A paradigm for semantic picture recognition is realized in the development of a general procedure which makes explicit use of contextual information and bases recognition on intensional class descriptions.

Summary.-Two forms of a novel paper-and-pencil encoding task were developed to measure proactive inhibition during learning when given to children and adolescents. The 2 forms of the encoding task were administered individually to 63 students referred for psychological services throughout a school district from a midwestern city. Results indicated that proactive inhibitory processes were present throughout learning and suggested that this encoding task could substitute, in special cases, for the classical verbal presenration of paired-associate materials that typically require individual administration. An attempt was made to make associative learning an intentional and integral component of a paper-and-pencil encoding task that could be easily and efficiently administered to individuals or large groups of Ss. Such a task, if developed, could substitute in special cases for the often tedious paired-associate technique typically requiring individual administration and a great deal of consideration on the part of the investigator as to the list construction, administration, and reliability. METHOD The combination of letters selected for this encoding task were chosen so that the total sum value of each of two paired-associate lists would approximate that of the other in terms of meaningfulness (m) , as described by Schulz and Underwood ( 1960). Stimulusresponse members were selected from tables presented by Schulz and Underwood for paired combinations of letters and so selected to produce two lists of word combinations with similar total sum value for m. Two experimental encoding tasks were developed utilizing these two lists of similarly scaled stimulus-response members. Each list was incorporated into one form of an encoding task (cf. Fig. 1). One task consists of the paired-members of List X and heretofore referred to as Form X of the encoding task. The Y list was incorporated into Form

In this paper [and in Joshi et al. (1972) which is a sequel to this paper] a new style of formal grammar called String Adjunct Grammars (AG) has been studied. The rules in an AG have a character essentially different from the “rewrite rule” in a Phrase Structure Grammar (PSG). Such a study of formal grammars of different styles is of great interest because each style is well suited for characterizing certain aspects of natural language structure but has inherent difficulty in characterizing certain other aspects. Several subclasses of AG's motivated by strong linguistic considerations have been studied. Linguistic relevance of these grammars and other grammars suggested by this study has also been discussed.

In this paper I shall define the concept logical information distinguished from a customary concept (semantic) information, which is called "factual information" in the paper. In accordance with the contemporary theory of semantic information, if i is an analytic sentence, then i conveys no information. It means thatinf (i) = -log m (i) = 0where i is an analytic sentence and inf is the measure of information. According to our intuition, however, it seems that most analytic sentences convey much more information than "A is A", which is a trivial analytic sentence or tautology. I shall give only one example here.1. Man is man. (S1)2. Man is a rational animal. (S2)Let us suppose that the both sentences Si and S2 are analytic. Then S2 has the same intention, and hence conveys the same amounts of factual information, as S1, whereas S2 has the more complicated intensional structure, and hence conveys much larger amounts of logical information, than S1.

For the storage of information for subsequent retrieval of desired items, two stages of analysis are essential. The first is the determination of the subject content of a given article or paper; the second is the selection of certain words, groups of words, or classification headings by which the subject content is to be represented, either directly or by a suitable coding. Some workers still look forward to the day when it will be possible for the whole of a text to be read and ‘understood’ automatically by a machine; the ‘understanding’ process has been envisaged either as a process of selection of terms by the measure of word frequency, or word‐pair frequency (adjacent terms or terms not too far separated in one sentence) in the text, or by some process of automatic linguistic analysis. Such methods appear unsuitable for several reasons: language, as normally used, is a very difficult medium for exact expression (hence the value of mathematics) and few authors write well enough to avoid all ambiguities; a human reader accustomed to the subject can easily overcome any difficulties due to poor grammar, badly expressed arguments, excess brevity or prolixity in writing and even, sometimes, actual errors; a machine can not do so. Furthermore, the content of a paper is rarely of uniform importance throughout, and it is not worth recording, for subsequent retrieval, details which are merely repetitions of matters described earlier and better elsewhere, and not essential to the main purpose of the paper; for example, in a paper on evaporator design, a description of a standard method of analysis, applied to the contents of the evaporator in determining the efficiency of the design, will not be worth indexing; in a search for analytical methods, retrieval of such a paper would hardly be considered pertinent. A human reader, though far from infallible, can usefully make such judgments.