English algorithmic grammar

[...]... It is to be noted, however, that the results achieved by the algorithmic procedures described in this study by far exceed the results for the English language obtained by Primov and Sorokina (1970) using the same method (To prevent unauthorized commercial use the authors published only the blockscheme of the algorithm.) 4 ENGLISH ALGORITHMIC GRAMMAR 2 Basic assumptions and some facts It is a well known... No 46 in List No 1, and identified as a NG (No 47): 10 ENGLISH ALGORITHMIC GRAMMAR ago = NG Following is the word was, which is recognized as such for the first time in operation No 38 After some brief exploration of its surroundings the program decides that was belongs to the VG: was = VG Next in sequence is the word divided Step by step, the algorithmic procedures pass it on to operation No 55, because... to the test many sentences in the way described above (following the algorithmic instructions), to prove for himself (herself) the accuracy of our description Though this is a description designed for computer use (to be turned into a computer software program), nevertheless it will surely be quite interesting 12 ENGLISH ALGORITHMIC GRAMMAR for a moment or two to put ourselves on a par with the computer... phrases 17 3 Algorithmic recognition of Parts of Speech One of the first grammatical difficulties found in using English as an input language in a computerized Text Processing system is the recognition of Parts of Speech in a text In this chapter, an algorithmic procedure is offered for recognition of Parts of Speech in the text, capable of yielding 99.93 per cent correct results The algorithmic procedure... (AA), Verb or Noun or Participle (VNP) for example run, abode The reader can find more examples in any English language dictionary that lists the Part(s) of Speech of a word NB Explanation of the terms covered by PANV and PAV: 1 Participle -1, as a Verb in a Compound Tense: 20 ENGLISH ALGORITHMIC GRAMMAR He was building a house 2 Gerund -1 (functioning as Noun): The building of a new society 3 Gerund... deal only with the grammatical information of the word, not with the word itself Then we compare the grammatical information of the word with the grammatical information required by the 24 ENGLISH ALGORITHMIC GRAMMAR algorithmic instructions, step by step: it is not a full stop (No 3), it is not a Preposition or an Article (No 5), it is not a figure or a Numeral (No 7), etc., till we finally arrive at... blank 1 Algorithmic recognition of the Verb In the present study an attempt is made to describe the Verb in the English sentence formally for the computer, by means of flow charts The flow charts are procedures for text analysis These procedures are based on the formal grammatical and syntactical features called 'markers' present in the text The procedures, in the form of instructions (in English) ,... longer the space left before the phrase, the more self-sufficient and independent the phrase is thought to be In this study we have established five types of phrases, depending on their relative ENGLISH ALGORITHMIC GRAMMAR 14 independence within the sentence This independence is expressed by a particular Auxiliary word (or words) or by a Punctuation Mark The longest and the most self-sufficient and relatively... 11, 13, 15, 17 or 19, the analysis yields no result until the program gets to operation No 21, where the word many is located in List No 7 Here the program, through operation No 22, checks 16 ENGLISH ALGORITHMIC GRAMMAR whether many is followed by yet another word from the Lists Operation 22ab certifies that it is not, and instructs the program to cut the sentence at this point and to leave three spaces... dog, ray, hot, top, via, why Mrs , etc List No 2: was, are, not, get, got, bid, had, did, due, see, saw, lit, let, say, met, rot off, fix, lie, die, dye, lay, sit, try, led, nit, , etc 6 ENGLISH ALGORITHMIC GRAMMAR (iii) List No 3: pay, dip, bet, age, can, man, oil, end, fun, dry, log, use, set, air, tag, map, bar, mug, mud, tar, top, pad, raw, row, gas, red, rig, fit, own, let, aid, act, cut, tax, .