Algorithmic recognition of the Verb

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ALGORITHMIC RECOGNITION OF THE VERB

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Министерство образования Республики Беларусь

Учреждение образования

«Гомельский государственный университет

им. Ф. Скорины»

Филологический факультет

Курсовая работа

Algorithmic recognition of the Verb

Исполнитель:

Студентка группы К-42

Марченко Т.Е.

Гомель 2005

Content

Introduction

Basic assumptions and some facts

1 Algorithm for automatic recognition of verbal and nominal word groups

2 Lists of markers used by Algorithm No 1

3 Text sample processed by the algorithm

Examples of hand checking of the performance of the algorithm

Conclusion

References

Introduction

The advent and the subsequent wide use of formal grammars for text synthesis and for formal representation of the structure of the Sentence could not produce adequate results when applied to text analysis. Therefore a better and more suitable solution was sought. Such a solution was found in the algorithmic approach for the purposes of text analysis. The algorithmic approach uses series of instructions, written in Natural Language and organized in flow charts, with the aim of analysing certain aspects of the grammatical structure of the Sentence. The procedures - in the form of a finite sequence of instructions organized in an algorithm - are based on the grammatical and syntactical information contained in the Sentence. The method used in this chapter closely follows the approach adopted by the all-Russia group Statistika Rechi in the 1970s and described in a number of publications (Kovcrin, 1972: Mihailova, 1973; Georgiev, 1976). 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 block-scheme of the algorithm.)

Basic assumptions and some facts

It is a well known fact that many difficulties are encountered in Text Processing. A major difficulty, which if not removed first would hamper any further progress, is the ambiguity present in the wordforms that potentially belong to more than one Part of Speech when taken out of context. Therefore it is essential to find the features that disambiguate the wordforms when used in a context and to define the disambiguation process algorithmically. As a first step in this direction we have chosen to disambiguate those wordforms which potentially (when out of context, in a dictionary) can be attributed to more than one Part of Speech and where one of the possibilities is a Verb. These possibilities include Verb or Noun (as in stay), Verb or Noun or Adjective (as in pain, crash), Verb or Adjective (as in calm), Verb or Participle (as in settled, asked, put), Verb or Noun or Participle (as in run, abode, bid), Verb or Adjective or Participle (as in closed), and Verb or Noun or Participle or Adjective (as in cut). We'll start with the assumption that for every wordform in the Sentence there are only two possibilities: to be or not to be a Verb. Therefore, only provisionally, exclusively for the purposes of the present type of description and subsequent algorithmic analysis of the Sentence, we shall assume that all wordforms in the Sentence which are not Verbs belong to the non-verbal or Nominal Word Group (NG). As a result of this definition, the NG will incorporate the Noun, the Adjective, the Adverb, the Numeral, the Pronoun, the Preposition and the Participle 1st used as an attribute (as in the best selected audience) or as a Complement (as in we'll regard this matter settled). All the wordforms in the Sentence which are Verbs form the Verbal Group (VG). The VG includes all main and Auxiliary Verbs, the Particle to (used with the Infinitive of the Verb), all verbal phrases consisting of a Verb and a Noun (such as take place, take part, etc.) or a Verb and an Adverb (such as go out, get up, set aside, etc.), and the Participle 2nd used in the compound Verbal Tenses (such as had arrived). The formal features which help us recognize the nominal or verbal character of a wordform are called 'markers' (Sestier and Dupuis, 1962). Some markers, such as the, a, an, at, by, on, in, etc. (most of them are Prepositions), predict with 100 per cent accuracy the nominal nature of the wordform immediately following them (so long as the Prepositions are not part of a phrasal Verb). Other markers, including wordform endings such as -ing and -es, or a Preposition which is also a Particle such as to, etc., when used singly on their own (without the help of other markers) cannot predict accurately the verbal or nominal character of a wordform. Considering the fact that not all markers give 100 per cent predictability (even when all markers in the immediate vicinity of a wordform are taken into consideration), it becomes evident that the entire process of formal text analysis using this method is based, to a certain degree, on probability. The question is how to reduce the possible errors. To this purpose, the following procedures were used: a) the context of a wordform was explored for markers, moving back and forth up to three words to the left and to the right of the wordform; b; some algorithmic instructions preceded others in sequence as a matter of rule in order to act as an additional screening; no decision was taken prematurely, without sufficient grammatical and syntactical evidence being contained in the markers; no instruction was considered to be final without sufficient checking and tests proving the success rate of its performance. The algorithm presented in Section 3 below, numbered as Algorithm No 1 i.Georgicv, 1991), when tested on texts chosen at random, correctly recognized on average 98 words out of every 100. The algorithm uses Lists of markers.

Algorithm for automatic recognition of verbal and nominal word groups

The block-scheme of the algorithm is shown in Figure 1.1.

Output result: attribution of the running word to one of the groups (verbal or nominal)Figure 1.1 Block-scheme of Algorithm No 1 Note: The algorithm. 302 digital instructions in all, is available on the Internet (see Internet Downloads at the end of the book).

1 Lists of markers used by Algorithm No 1

(i) List No 1: for, nei, two, one, may, fig, any, day, she, his, him, her, you, men, its, six, sex, ten, low, fat, old, few, new, now, sea, yet, ago, nor, all, per, era, rat, lot, our, way, leg, hay, key, tea, lee, oak, big, who, tub, pet, law, hut, gut, wit, hat, pot, how, far, cat, dog, ray, hot, top, via, why, Mrs, ..., etc. (ii) 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. (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, put, ..., etc.

(iv) List No 4: to, all, thus, both, many, may, might, when, Personal Pronouns, so, must, would, often, did, make, made, if, can, will, shall, ..., etc.

(v) List No 5: when, the, a, an, is, to, be, are, that, which, was, some, no, will, can, were, have, may, than, has, being, made, where, must, other, such, would, each, then, should, there, those, could, well, even, proportional, particular(ly), having, cannot, can't, shall, later, might, now, often, had, almost, can not, of, in, for, with, by, this, from, at, on, if, between, into, through, per, over, above, because, under, below, while, before, concerning, as, one, ..., etc.

(vi) List No 6: with, this, that, from, which, these, those, than, then, where, when, also, more, into, other, only, same, some, there, such, about, least, them, early, either, while, most, thus, each, under, their, they, after, less, near, above, three, both, several, below, first, much, many, zero, even, hence, before, quite, rather, till, until, best, down, over, above, through, Reflexive Pronouns, self, whether, onto, once, since, toward (s), already, every, elsewhere, thing, nothing, always, perhaps, sometimes, anything, something, everything, otherwise, often, last, around, still, instead, foreword, later, just, behind, ..., etc. (vii) List No 7: Includes all Irregular Verbs, with the following wordforms: Present, Present 3rd person singular, Past and Past Participle. (viii) List No 8: -ted, -ded, -ied, -ned, -red, -sed, -ked, -wed, -bed, -hed, -ped -led, -ved, -reed, -ced, -med, -zed, -yed, -ued, ..., etc.(ix) List No 9: -ous, -ity, -less, -ph, -'s (except in it's, what's, that's, there's, etc.), -ness, -ence, -ic, -ее, -ly, -is, -al, -ty, -que, -(t)er, -(t)or, -th (except in worth), -ul8, -ment, -sion(s), ..., etc. (x) List No 10: Comprises a full list of all Numerals (Cardinal and Ordinal).

2 Text sample processed by the algorithm

Text Word Group

She NG

Nodded VG

Again and NG

Patted VG

My arm, a small familiar gesture which always NG

Managed to convey VG

Both understanding and dismissal. NG

3 Examples of hand checking of the performance of the algorithm

Let us see how the following sentence will be processed by Algorithm No 1, word by word: Her apartment was on a floor by itself at the top of what had once been a single dwelling, but which long ago was divided into separately rented living quarters. First the algorithm picks up the first word of the sentence (of the text), in our case this is the word her, with instruction No 1. The same instruction always ascertains that the text has not ended yet. Then the algorithm proceeds to analyse the word her by asking questions about it and verifying the answers to those questions by comparing the word her with lists of other words and Punctuation Marks, thus establishing, gradually, that the word her is not a Punctuation Mark ('operations 3-5), that it is not a figure (number) cither (operation 5 7i, and that its length exceeds two letters (operation 8). The fact that its length exceeds two letters makes the algorithm jump the next procedures as they follow in sequence, and continue the analysis in operation No 31. Using operation No 31 the algorithm recognizes the word as a three-letter word and takes it straight away to operation No 34. Here it is decreed to take the word her together with the word that follows it and to remember both words as a NG. Thus: Her apartment~NG Then the algorithm returns again to operation No 1, this time with the word was and goes through the same procedures with it till it reaches instruction No 38, where it is seen that this word is in fact was. Now the algorithm checks if was is preceded (or followed) by words such as there or it (operation No 39, which instructs the computer to compare the adjacent words with there and it), or if it is followed up to two words ahead by a word ending in -ly or by such words as never, soon, etc., none of which is actually the case. Then, finally, operation No 39d instructs the computer to remember the word was as a VG

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