Guylaine BRUN-TRIGAUD IE1 CNRS Laboratoire Bases-Corpus-Langage (UMR 7320) Université de Nice Sophia Antipolis (France) Texte original : "Un usage particulier de l'algorithme de Damereau-Levenshtein dans le domaine occitan", in Toujours langue varie ... Mélanges Andres Kristol, édités par F. Diemoz et D. Aquino-Weber, Droz, Genève, 2014, 127-147. An unforeseen use of the Damerau-Levenshtein algorithm in the Occitan area 1 Introduction When I had finished entering into our basis the data of the last maps of the regional linguistic atlases published by the CNRS, which constitute the main source of the Thesaurus Occitan (THESOC) (nearly 800,000 data are currently available online 1 ), the scientific project leaders, Jean-Philippe Dalbera and Michele Olivieri entrusted me with the task of completing this corpus with unpublished data from Alloc and Allor 2 . Until then the THESOC data had always been collected in published maps, which allowed me a frequent use of the copy and paste functions, especially in a relatively homogeneous area like Alloc 3 . But this solution seemed no longer possible since the data were to be picked up directly from the survey notebooks (it is impossible to check at the same time the answers for the same concept from each of the 131 notebooks, even in the form of scanned images,...). So as to save time without embarking on the chore of taking one by one, for each of the 131 survey items, the 430 new lexical concepts 4 to input (i.e. a potential 56,330 forms), I had the idea to apply Camps's dialectometric method (1986) to Alloc.

Following the so-called "global" method established by [START_REF] Guiter | Atlas et frontières linguistiques[END_REF], in which each point is connected to its nearest neighbours by the conventional triangulation system used in this discipline 5 , Camps proceeded to count the differences for each segment and built a full table of data 6 (1986, p. 125-131). The purpose of his study was to prove the existence of dialect boundaries based on the results achieved.

Using that table as a basis, I spotted the segment which showed the smallest number of differences for each survey point, which helped me build a network connecting the points two 1 http://thesaurus.unice.fr/.

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. In 1996 CNRS put an end to the publication of the Regional Linguistic Atlas of France. At that time, some

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. In 1996 CNRS put an end to the publication of the Regional Linguistic Atlas of France. At that time, some atlases had not yet been completed and a large number of data remained inaccessible. Now thanks to THESOC some of them are searchable (such as data from Allor 3 and ALAL 4 which had been computerized but not published) or will be accessible in the near future (they consist of unpublished data from Alloc which will be discussed here,from Allor and from ALP). 3 Cf. Brun-Trigaud, Darlu et alii (forthcoming). 4 Nominal and verbal morphologies have already been entered and will be added shortly to the THESOC data. The unpublished lexical fields are distributed as follows: human body (end of chapter), timeline (days of the week, etc.), "from cradle to grave" (children's games, diseases, qualities, defects, etc.), kinship, religion and religious festivals, trades and social life 5 See in particular [START_REF] Goebl | Introduction aux problèmes et méthodes de l'«École dialectomé-trique de Salzbourg» (avec des exemples gallo-, italo-et ibéroromans)[END_REF] for a detailed presentation. 66 This table is unfortunately not available in the study conducted for Allor [START_REF] Camps | Limites linguistiques en Languedoc oriental[END_REF]. Only the results map is found in it, which means that perhaps I will not be able to repeat this experience for unpublished data from this atlas . by two, in all 130 segments. Map 17 , drawn starting from Camps's data, where the thickness of the arrow connecting the two points is proportional to the gross differences8 , shows clearly that the difficulties would be greater in the northern part of the area ... Map 1: network established from the raw data [START_REF] Camps | Limites linguistiques d'après l'ALLOc[END_REF] After I had entered all the data from the first point (the choice fell on Toulouse (31.12) because of its central position), the next step was to continue as before using the copy and paste function, changing nothing but the items that were different for each point, while following the network established above. I was thusd able to enter 56,000 data in a relatively short time 9 , which shows that dialectometry may also be put to serve purposes different from those we are familiar with... I took that opportunity to experience how dialectometric methods could help me with my work on that area starting from a small corpus.

2 Following Guiter's steps ... To start with, I compared the two corpora: Camps had been working on a set of 100 maps with minimal gaps from the first volume of Alloc 10 . I likewise selected the 208 complete "maps 11 " (i.e. with no gaps) available in my corpus.

I applied to them the same comparison method Camps had used, that is to say, counting only one difference whatever its nature (either lexical or phonetic) or the number of phonetic variants between the two segments 12 , focusing on the nearest form, in the case of multiple responses. The attached table (columns B, C and D) shows the percentage results for each of the 130 segments in both corpora.

One is immediately struck by the fact that the number of differences between segments is larger in my corpus than in Camps's (see attached table, col B and C.). What accounts for this disparity? [START_REF] Guiter | Appréciation de l'importance des écarts en géolinguistique[END_REF] had studied the issue of differences within his own method and deduced that statistically a 1% difference between two measurements (what he considered to be highly unlikely) was not significant. We find the same proportion here, which means that is nothing went amiss 13 .

... Then following Séguy's

One cannot obviously talk about dialectometry without mentioning J. Séguy who coined the term in 1973. His method was quite similar to Guiter's, except that he decided to set apart phonological, phonetic, morphosyntactic and lexical variations whose average was then weighted (see vol. 6 ALG).

I have myself treated apart phonetic and lexical variations so as to determine their respective roles in the understanding of the differences between the segments. Séguy rightly states: "The fact remains that the lexicon is the key to comprehension: we only have to recognize a number of words in a language we do not know, to understand what a statement is about; conversely, if in a language we know we encounter an enigmatic word, this leaves a gap in the message that the context does not always allow us to fill. " (1971,339).

The lexical gaps identified on the segments of the network range from 2-21% with an average value of 10% (see the attached table, col. E and Map 2). The most important values (dark gray 99 It took me only four months when normally more than six are required for the same amount of input. 1010 Camps, 1986, 123. 1111 The use of the word "map" although unfit here (no map was currently created with the data) is intended to remain in the familiar world of geolinguistic concepts. 1212 In fact this rule is quite implicit in Ch Camps, as in H. Guiter. "[...] We counted the number N of differences between 2 points" (Camps 1986, 120) or "network triangulation having been prepared, we have offered to investigate how often each segment was cut by a isogloss line " (Guiter, 1973, 67) or" between each pair of connected points, we count the number of differences that occur, regardless of the nature of the difference(s) found. Hence the name "comprehensive approach" given to this extremely fast process. " (Guiter, 1991, 101). 13 Using Camps's data, with the help of THESOC, it appears that the difference could be that the distinction between R and has not always been taken into account.

in the table and black on the map) were mainly found in the periphery, in Ariège, Aude (ex 09.10> 09.01 14 .: 16%) but also in Lot, Lot-et-Garonne, Tarn and eastern Dordogne (ex 47.10> 24.32. 21%), which paradoxically also have some of the lowest values (light gray in the table and on map 2) (ex 24.31> 24.33. 2%), which are also found in western Tarn (ex 81.07> 81.05. 3%). Thus the lexicon is not really discriminating, except in the periphery, particularly at the southern end where there is a significant accumulation of relatively high values.

Map 2: lexical differences (maximum and minimum values) Phonetic differences fall between 10 and 78% with an average of 33% (see the attached table, col. F and map 3) if we continue to use Guiter's "global method" (difference = 1, regardless of the nature or number of differences). Not surprisingly, the lowest are found in the central and southern areas (Tarn, Haute-Garonne, Tarn-et-Garonne and the northern departments of Ariège and Aude) (light gray in the table and on map 3), but the largest differences (in dark gray in the table and in black on map 3) lie mainly in the North (Gironde, Lot-et-Garonne, Lot and Dordogne). The only exception is segment 11.22> 09.33 in contact with the Catalan-speaking zone. This area, lexically more marked as we have just seen, does not particularly stand out from a phonetic viewpoint. A comparison between maps 2 and 3 shows that there is a certain degree of correlation between the proportion of lexical and phonetic differences (low in the centre of the field, strong in the North). Yet some segments show some discrepancies (see 46.20> 46.21 or 82.04> 82.03).

Map 3: phonetic differences (maximum and minimum values)

4 A new track with the Damerau-Levenshtein algorithm I have attempted a more detailed analysis of the phonetic facts using the Damerau-Levenshtein algorithm, which was notably introduced by the Groningen dialectometry school under the leadership of J. Nerbonne and W. Heeringa 15 . This algorithm allows to measure the distance between two strings based on the minimum number of deletions and/or insertions and/or replacements and/or character transpositions involved to switch from one string to another. We have added a new function 16 allowing to return the value of the characters affected by the measurement between the two compared strings, and so to evaluate, for each phoneme of each segment, the total number of operations required on the corpus under consideration. This is to determine whether the score is high because of the number of different variations or because of the frequent repetition of the same phenomenon. The appeal of this method is that it also allows to analyze the words or phrases in their entirety, as they are heard by the speakers. 16 In fact, the functions based on the Damerau-Levenshtein algorithm available online (see in particular the Nerbonne and Heeringa demo site, http://www.let.rug.nl/kleiweg/lev/) return a score (number of operations necessary to move from one string to another), but not the value of the modified characters. We used the version of http://stackoverflow.com/questions/4243036/levenshtein-distance-in-excel,-and I thank P. A. Georges and S. Brun, computer scientists, for their invaluable assistance. In the segments with small phonetic differences, we notice no remarkable value for the first operation (col F, I,. Small gray rectangles, Map 4), see 31.12> 31.01, but rather a dispersion. In contrast, in the segments with average values, particularly in the south of Lot and Lot-et-Garonne as well as in Tarn, we notice the presence of some notable variations (col I;. Black arrows in gray areas, map 4) see 31.32> 81.23 or 82.11> 12.03.

Finally, the phonemes plotted on map 5 show a strong presence of alternating r (:)/ and a/ in the northern area, as foreshadowed in table 2, while eastern Tarn is characterized by the presence of d/ð. Note also that some points are the centre of a large number of variations such as. 46.14 or 24.20 which stand out from their neighbours.

Map 4: phonetic differences and proportion of the main variation

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-&-%P-&%- %1 %& Q 0 %) Q% R. de $ par l int. ,4 Ins. de s R R. de $ par l 4 )- -&,-P-&-, %R )1 0 ,R %, 11 R. de d! par ! 4 R. de t" par d! 4 R. de $ par l Q ,& -&,-P-&-% %% %, S, 4 ,Q )1 R. de t" par d! ,% Ins. de % ) R. de & par e ) ,0 -&%-P-&%, %Q %& 1 ,% ,& )0 R. de l par l int. ,, R. de ts par d! 0 S. de s 0 %4 -&%-P-&)- )% )% - & %Q 4% R. de ! par ' ,) R. de ! par d! R S. de % R %Q -&%,P-&%% )- )Q Q ,) %1 QQ R. de l int. par l ,, R. de ' par d & R. de & par o 4 %4 -&%,P-&), )- )4 4 ,) %4 Q1 R. de ( par e ,4 R. de l int. par l & R. de ' par d 4 )) -&%%P-&)% Q% 11 S0 ,) )1 40 R. de l par $ ,1 R. de l it. par $ R S. de t R %R "! #!$%&'! (!$)&'! *! +! $,-'! .! $))'! /!$01'! 2! 3! $,4'! 5! 6! $&'! 7! 8!$4'! 9!$))
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%& QQ %R ,- Q- ,%) R. de -par s ,4 R. de , par a & R. de e par ( 4
)) %1),P%1)) %R )- 1 % %0 4% R. de e par ( %Q R. de & par a 4 R. de " par - 4 )& %1)%P%1%, )0 QQ ,4 ,% 1& ,%% R. de . par ' ,4 R. de " par - 4 R. de * par v R )- %1))P%1)- %) )0 ,Q ) )Q &4 R. de " par - & R. de ( par e 0 R. de & par a R %) ),-,P0%%1 %- %1 1 ,% %R R, R. de ( par e ,4 R. de w par l 0 Ins. de s Q )- ),,-P0%)- ,& )) ,1 ,- %Q 1& S. de j ,R Ins. de " 4 R. de ts par t" Q %0 ),,,P0,%- %- ), ,, ,- %) R) R. de d par ' %1 R. de t" par c ,-R. de d! par ! 1 )0 ),,%P),-, ,4 )R ,& ,- %& R& R. de l par w R S. de r R R. de $ par j 1 ,R ),,%P),,, ,Q %% 4 ,- ,) %& Ins. de s 4 R. de ' par d 4 R. de r par r# 4 %, ),,%P),%- ,1 %Q ,, ,- ,R 1- Ins. de t 0 R. de ' par d Q Ins. de % Q ,0 ),%-P),%, %, )- & 0 %, 1R R. de l par $ ,) R. de d par ' 4 R. de % par 1 4 %4 ),%-P),), ,& %) 1 ,- ,1 )
Q R. de d! par j R R. de e par u R R. de r par r# R ,0 ),)-P),)% %1 )Q ,, ,- %0 R1 R. de 2 par ts )-R. de c par ts & R. de ! par ts R 1Q ),
& S. de j R %1 1R)-P1R), )0 )4 S, 0 ), 0- R. de s par " ,R Ins. de n ,) R. de & par a & )0 1R)-P1R)) )1 1& ,Q ,R )& 0Q Ins. de s ,1 R. de " par s ,1 R. de & par a 4 )Q 1R),P1R)% 14 4R %& ,, 41 %-Q R. de r(#) par 4 )4 R. de " par s 0 R. de ts par t" R Q, 1R)%P,%-% 14 0- )) ,- 40
0,-)P0,-, ,Q ,& 1 4 ,) %0 S. de t ,, R. de % par 1 4 R. de r par r# 4 %Q 0,-)P0,-R %) %) - 4 ,4 )4 Ins. de 1 0 R. de ' par r 0 S. de t 0 %1 0,-QP0,-) %, )) ,% Q %& RQ R. de d par ' %Q R. de 4 par r(#) %) R. de % par 1 R Q1 0,-4P0,-Q ,0 ,& , ) ,R 1- R. de r# par 4 )0 R. de d par r 0 R. de b par * Q Q, 0,,-P,%%- )1 1, 4 & )Q &) R. de ' par d ,R R. de 1 par % R R. de 5 par s Q %4 0,,-P,%%1 )0 1, ) & )Q &- R. de 5 par s ,% R. de r par l ,% R. de ' par d R )- 0,,-P0,

Map 5 :

 5 Key phonetic variations 5 Conclusions Inputting unpublished Alloc lexical data has allowed me to test a personal version of the Damerau-Levenshtein algorithm on a limited number of pairs of locations as they were determined by the initial choice of the corpus. Beyond the accumulation of numbers usually generated by statistic measurements it seemed interesting to materialize the obstacles to intercomprehension between the various segments studied. It would be instructive to extend this research to all the segments in a full triangulation, which would reveal further divisions iside the studied area 17 . (Figures in brackets indicate the average) B : C. Camps differences (gross) in % C: GBT differences in % D : Difference between Camps and GBT E : GBT lexical differences % F : GBT phonetic differences % G : GBT total number of operations H : operation 1 and I : H / G J : operation 2 and K : J / G L : operation 3 and M : L /G N : I + K + M Abbreviations for H, J, L : R. = replacement ; S. = suppression ; Ins. = insertion Light gray boxes: lowest values Dark-gray boxes: highest values

Table 2 :

 2 Most frequent alternationsNote that, despite the relatively small numbers, the above 22 examples include half of the 400 recorded total unique variations detected. What happens at segment level? An inventory of the three most common operations for each segment is provided in the attached table. Some interesting facts have been extracted here:

	A	F	H	I	J	K	L	M	N
	segment	diff. ph. %	operation 1	%	operation 2	%	operation 3	%	total %
	2411>2410 55	Rep. ð by d	7	Rep. ɛ by e	7	Rep. ʃ by ɕ	6	20
	3112>3101 29	Rep. de l par w 6	S. de r	6	Rep. de ʎ par j 4	16
	3132>8123 27	Rep. de ts par ʒ 32	Rep. de d par ð 19	Rep. de ɡ par ɣ 6	57
	4632>1202 78	Rep. de ʀ par r(ː) 31	Rep. de tʃ par cʃ 7	Ins. de s	6	44
	4710>2432 68	Rep. de s par ʃ	15	Rep. de ts par θ 13	Rep. de d par ð 11	39
	4722>4720 71	Rep. de r(ː) par ʀ 36	R. de ɔ par oe	22	Rep. de e par oe 10	68
	8211>1203 39	Rep. de a par ɔ 32	Rep. ˢ par ʰ	7	Rep. s by ˢ	6	45

Table 3 :

 3 Sample breakdown by segmentTwo possible explanations can account for a large phonetic difference (col F.): First, most cases in the northern area result from an accumulation of variations (col I, K, M, large rectangles +. small gray lines, map 4), see e.g. 47.10> 24.32 or 24.11> 24.10 representing an extreme case of scattering. Secondly, a lesser represented difference, the preponderance of a single variation (neck. F, I, big gray rectangles, map 4), see 46.32> 12.02 or two, see 47.22> 47.20, is reflected on the eastern and western margins of the area and the segment neighbouring the Catalan area (11.22> 09.33).

The maps were produced using the software Cartes & Données de Arctique and a good help by Photoshop ...

To maintain the comparability of the facts, only the raw data set by Camps were used. On the other hand, I acknowledge that I am aware of my unconventional use of rays cards, since these are normally used to account for the gradient of similarity between two points (see in particular Goebl 2012) . But I do not work within a conventional dialectometry with triangulation network, but in a two-to-two relationship, hence the choice of this cartographic expression that can account for both similarity and distance set in the thickness of the lines connecting the points.

The first location quoted served as a "model" for the second.

I also plan in the near future, with the support of Hans Goebl, to test the data using Salzburg's DM system.