Was =VG
And to return to the start again, this time with the next word on. Going through the initial procedures again, our hand checking of this algorithm reaches instruction No 9 where it is made clear that the word is indeed on. Then the algorithm checks the left surroundings of on, to see if the word immediately preceding it was recognized as a Verb (No 10), excluding the Auxiliary Verbs. Since it was not (was is an Auxiliary Verb), the procedure reaches operation Nos 12 and 12a, where it becomes known to the algorithm that on is followed by a. The knowledge that on is followed by an Article enables the program to make a firm decision concerning the attribution of the next two words (12a): on and the next two words are automatically attributed to the NG:
On a floor NG
After that the program again returns to operation No 1, this time to analyse the word by. The analysis proceeds without any result till it reaches operation No 11. Where the word by is matched with its recorded counterpart (see the List enumerating the other possibilities). In a similar fashion (see on), operation No 12b instructs the computer to take by and the next word blindfoldedly (i.e. without analysis) and to remember them as a NG. Thus we have:
By itself= NG
We return again to operation No 1 to analyse the next word at and we pass, unsuccessfully, through the first ten steps. Instruction No 11 enables the computer to match at with its counterpart recorded in the List (at). Since at is followed by the (an Article), this enables the computer to make a firm decision: to take at plus the plus the next word and to remember them as a NG:
At the top =NG
We deal similarly with the next word - of - and since it is not followed by a word mentioned in operation No 12, we take only the word immediately following it (12b) and remember them as a NG:
Of what --NG
Since the next word - had - exceeds the two-letter length (operation No 7), we proceed with it to operation No 31, but we cannot identify it till we reach operation No 38. Operation No 39 checks the immediate surroundings of had, and if we had listed once with the other Adverbs in 39b, we would have ended our quest now. But since once is not in this list, the algorithm proceeds to the next step (39d) and qualifies had as a VG:
Had =VG
Now we proceed further, starting with operation No 1, to analyse the next word, once. Being a long word once jumps the analysis destined for the shorter (two- and three-letter) words and we arrive with it at operation No 55. Operations No 55 and 57 ascertain that once does not coincide with either of the alternatives offered there. Through operation No 59 the computer program finds once listed in List No 6 and makes a correct decision - to attribute it to the NG:
Once =NG
Now we (and the program) have reached the word been in the text. The procedures dealing with the shorter words are similarly ignored, up to operation No 61, where been is identified as an Irregular Verb from List No 7 and attributed (No 62b) to the VG:
Been =VG
Next we have the word a (an Indefinite Article) which leads us to operations No 11 and 12 (where it is identified as such), and with operation No 12b the program reaches a decision to attribute a and the word following it to the NG: a single -- NG Next in turn is dwelling. It is somewhat difficult to tag, because it can be either a Verb or a Noun. We go with it through all the initial operations, without significant success, until we get to operation No 69 and receive the instruction to follow routines No 246-303. Since dwelling does not coincide with the words listed in operation No 246, is not preceded by the syntactical construction defined in No 248 and does not have the word surroundings specified by operations No 250, 254, 256, 258, 260, 262, 264, 266, 268, 270, 272. 274, 276, 278 and 280, its tagging, so far, is unsuccessful. Finally, operation No 282 finds the right surrounding - to its left there is, up to two words to the left, an Article (a) - and attributes dwelling to the NG:
Dwelling ~NG
However, in this case dwelling is recognized as a Gerund, not as a Noun. If we were to use this result in another program this might lead to problems. Therefore, perhaps, here we can add an extra sieve in order to be able to always make the right choice. At the same time, we must be very careful when we do so, because the algorithms arc made so compact that any further interference (e.g. adding new instructions, changing the order of the instructions) might well lead to much bigger errors than this one. Now, in operation No 3, we come to the first Punctuation Mark since we started our analysis. The Punctuation Mark acts as a dividing line and instructs the program to print what was stored in the buffer up to this moment. Next in line is the word but. Being a three-letter word it is sent to operation No 31 and then consecutively to Nos 34, 36, 38 and 40. It is identified in No 42 and sent by No 43 to the NG as a Conjunction:
But =NG
Next, we continue with the analysis of the word which, starting as usual from the very beginning (No 1 ) and gradually reaching No 55, where the real identification for long words starts. The word which is not listed in No 55 or No 57. We find it in List No 6 of operation 59 and as a result attribute it to the NG:
whuh - NG
The word long follows, and in exactly the same way we reach operation No 55 and continue further comparing it with other words and exploring its surroundings, until we exhaust all possibilities and reach a final verdict in No 89:
long -= NG
Next in turn is the word ago. As a three-letter word it is analysed in operation No 31 and the next operations to follow, until it is found by operation No 46 in List No 1, and identified as a NG (No 47): 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: ext in sequence is the word divided. Step by step, the algorithmic procedures pass it on to operation No 55, because it is a long word. Again, as in all previous cases, operations No 55, 56, 57, 59, 61 and 63 try to identify it with a word from a List, but unsuccessfully until, finally, instruction No 65 identifies part of its ending with -ded from List No 8 and sends the word to instructions No 128-164 for further analysis. Here it does not take long to see that divided is preceded by the Auxiliary Verb was (No 130) and that it should be attributed to the VG as Participle 2nd (No 131):
divided = VG
The Preposition into comes next and since it is not located in one of the Lists examined by the instructions and none of its surroundings correspond to those listed, it is assumed that it belongs to the NG (No 89):
Into =NG
Next, the ending -ly of the Adverb separately is found in List No 9 and this gives enough reason to send it to the NG (No 64):
Separately =NG
Now we come to a difficult word again, because rented can be either a Verb or an Adjective, or even Participle 1st. Since its ending -ted is found in List No 8, rented is sent to instructions No 128-164 for further analysis as a special case. With instructions No 144 and 145 the algorithm chooses to recognize rented as a Participle (1st) and to attribute it to the NG:
Rented = NG
Next comes living. At first it also seems to be a special case (since it can be Noun, Gerund, Verb - as part of a Compound Tense - Adjective or Participle). Instruction No 69 establishes that this word ends in -ing and No 70 sends it for further analysis to instructions No 246-303. Almost towards the end (instructions No 300 and 301), the algorithm decides to attribute living to the acknowledging that it is a Present Participle. If the program were more precise, it would be able also to say that living is an Adjective used as an attribute. The last word in this sequence is quarters. The way it ends very much resembles a verbal ending (3rd person singular). Will the algorithm make a mistake this time? Instruction No 67 recognizes that the ending -s is ambiguous and sends quarters to instructions No 165 245 for more detailed analysis. Then the word passes unsuccessfully (unrecognized) through many instructions till it finally reaches instruction No 233, where it is evidenced that quarters is followed by a Punctuation Mark and this serves as sufficient reason to attribute it to the NG:
Quarters = NG
Finally, our algorithmic analysis of the above sentence ends with commendable results: no error. However, in the long run we would expect errors to appear, mainly when we deal with Verbs, but these are not likely to exceed 2 per cent. For example, an error can be detected in the following sample sentence: .Not only has his poetic fame - as was inevitable - been overshadowed by that of Shakespeare but he was long believed to have entertained and to have taken frequent opportunities oj expressing a malign jealousy oj one both greater and more successful than himself.
This sentence is divided into VG and NG in the following manner:
Text Word Group
Not VG
Only NG
Has VG
His poetic fame NG
As NG
Was VG
Inevitable NG
Been overshadowed VG
By that of Shakespeare NG
But he NG
Was long believed to have entertained VG
And NG
To have taken VG
Frequent opportunities of expressing NG
A malign jealousy of one both greater NG
More successful than himself. NG
As is seen in the above example, the word long was wrongly attributed to the VG (according to our specifications laid down as a starting point for the algorithm it should belong to the NG). The reader, if he or she has enough patience, can put 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 for a moment or two to put ourselves on a par with the computer in order to understand better how it works. Of course, that is not the way we would do the job. Our knowledge of grammar is far superior, and we understand the meaning of the sentence while the computer does not. The information used by the computer is extremely limited, only that presented in the instructions (operations) and in the Lists. Further on we will try to give the computer more information (Algorithm No 3 and the algorithms in Part 2) and correspondingly increase our requirements.
Conclusion
Most of the procedures to determine the nominal or verbal nature of the wordform, depending on its context, are based on the phrasal and syntactic structures present in the Sentence (for example, instructions 11 and 12, 67 and 68, 85, etc.), i.e. structures such as Preposition + Article + Noun; will (shall) + be + (Adverb) + Participle; to + be + (not) + Participle 2nd + to + Verb; -ing + Possessive Pronoun + Noun, etc. (the words in brackets represent alternatives).
When constructing the algorithm it was thought to be more expedient to deal first with the auxiliary and short words of two-letter length, then with words of three-letter length, then with the rest of the words - for frequency considerations and also because they represent the main body of the markers.
The approach presented in this study is not based on formal grammars and is to be used exclusively for text analysis (not for text synthesis). One should not associate the VP (Verbal Phrase) with the VG and the NP (Noun Phrase) with the NG - for these are completely different notions as has been shown by the presentation.
The algorithm can be checked by feeding in texts through the procedures (the instructions) manually and if the reader is dissatisfied he or she may change the instructions to improve the results. (See Section 3.3 for details of how the performance of the algorithms can be hand checked.) The algorithm can be easily programmed in one of the existing artificial languages best suited for this type of operation.
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