dataesr/scientific-paragraphs-categorization huggingface dataset:
Limits of disambiguation steps We found out that the formatting options of our AD script dataset do not always help distinguish stage directions from dialogue prompts. From a linguistic point of view, stage directions are quite heterogeneous: some of them are represented by a single noun like (KNALL) [(BANG)] or by truncated clauses: (kurz Motor) [(Motor short)], which makes it difficult to interpret their status (are they stage directions or dialogue prompts?) when they appear within speaker parts. Other stage directions provide more explicit instructions thanks to the use of action verbs, e. g. in the sample <("Ja" übersprechen)> [(Override "Yes")]. Finally, we wonder if we were right when we deleted the round brackets surrounding the letters 
, <ss>, and <n> indicating speed instructions; thanks to the presence of round brackets, they would be more clearly associated to stage directions.
Recommendations When normalising AD scripts, it can be helpful to use the following safe symbol or sign replacements: • (ss) > ss • (s) > s • )" > ) • "( > ( • ? " > ?" • ! " > !" In addition, we advise closing all word or text files before opening a new dataset with ATOM and saving all changes before reopening the files with the help of another tool. As a matter of fact, some of our requests provided better results with other text editors: ATOM did not provide any result when we searched our files for double quotation marks at the beginning of all lines, but our search for <\n> elements was successful when using the text editor Notepad++ (Don Ho, 2010). In the case of the treatment of multiple files, the text editor Geany (Brush & Tröger, 2020) helped us, better than Notepad++ and ATOM, understand which files were currently open and which changes were applied to the data through automatic replacement.
Conclusion Preparing text-based AD scripts to implement them into the corpus tool TXM helped us analyse the making of this complex text type. We understood that applying the corpus perspective to such data greatly reduces formal variance. This has advantages and disadvantages: one must carefully evaluate which of the pre-processing steps are necessary and helpful. The risk exists that some of them lead to simplified, poorly formatted texts, which do not offer enough recording instructions. What would happen if a voice talent tried to record the speaker parts with the help of our modified AD scripts?.
Aim of the paper Our aim is to go through this available disparate information in order to produce a comprehensive synthesis of our present knowledge on metal production and use in Greek Eastern Macedonia during the late 6 th and 5 th millennia BC, which is an important formative period not only here but across the entire Balkan peninsula  (Pernicka 1993; Mazanova 2004; Jovanovi≤ 2009; πljivar 2006; Hansen 2013; Kunze, Pernicka 2020a; Radivojevi≤, Roberts 2021)  and the Aegean (Zachos 1996a;  Papadatos et al. 2004; Bassiakos, Catapotis 2006; Kakavogianni et al. 2008; Zachos 2010) , as well as in Anatolia and the Near East  (Efe 2002; Maddin et al. 1999; Özdogan, Parzinger 2000; Rothenberg, Merkel 1998; Thornton 2001; Yalçın 2000; Yener 2000). More recent and still unpublished data are also included, mainly from the recent works at the tell settlement of Dikili Tash.
Limenaria An unexpectedly early metal find, a small fragment of a silver pin, was discovered in a late Middle/early Late Neolithic horizon (second half of the 6 th millennium BC) at the Lioudas plot (Fig. 10 ) (Papadopoulos,  Malamidou 1997. 836-837; Papadopoulos 2008. 65, Fig. 3 ). Compositional data and microstructural examination have shown that the pin was shaped by hammering of a lump of silver. Its internal micromorphology reveals that the silver is heterogeneous and consists of pure metal in which semitranslucent, minute pieces of slag are embedded. It is therefore presumed that the silver used to make the pin is not native, but the product of an extraction from argentiferous lead ore  (Bassiakos 2012. 210-211). No silver finds are recorded from the 5 th millennium, which is altogether less well documented on the site.
350 s. The modulation time was optimized depending on the distribution of chromatographic peaks in the twodimensional chromatogram. For the ease of reading, when a modulation time of 6 s was applied, a shift phase of -2. 0 s was applied during the treatment of data with the GC Image software. A multistep temperature program was used for the hot jet set at 200°C for 30 min and subsequently raised to 280°C until the end of the acquisition. The oven temperature was initially held 1 min at 81°C, and then ramped at 3°C min -1 to 325°C, where it was held for 25 min. The total duration of GC analysis was ca. 107 min. The injector was held at 280°C and used in split mode (1:30 of the total flow). The mass spectrometer was operated at 20,000 u. s-1, with a scan range from 50 to 500 u, using electron ionization at 70 eV.
The interface was kept at 300°C and the ion source at 200°C. Data processing of the 2D raw data was achieved using GC Image software, version 2. 4 (Lincoln, Nebraska). Identification of components was performed by comparing the mass spectra of unknown components with reference compounds from the NIST MS library (2011) and interpretation of the main fragmentations. The raw Trinidad Lake asphalt (TLA) was characterized first to serve as the reference material using the same experimental conditions which would be used later for samples from the individual steps in the reconstruction. Py-TMAH-GC/MS analysis showed a series of typical markers and biomarkers. These were defined as features to be tracked in the oil paint reconstruction samples (see Fig. 3  and Table  2 ).
Acknowledgements CORES Ph. D. programme  PD/00253/2012  and  LAQV-Requimte UIDB/50006/2020  and  UIDP/50006/2020. Special thanks to  Dr. Klaas Jan van den Berg  for facilitating access to the Py-TMAH-GC/MS equipment at the  RCE (Cultural Heritage Agency of the Netherlands)  and to  Henk van Keulen  for performing the Py-TMAH-GC/MS analyses. The authors would also like to acknowledge the help of  Davy Safrano  who run the Py-GCxGC/MS analysis at the  CRCC (Centre de Recherche sur la Conservation des Collections ) in Paris.
All data were gathered by interviewing a single NORM speaker (Non-mobile, Old, Rural Men) per dialect following a written questionnaire. The ALF and AIS questionnaires include 46 negative sentences (24 in Italian and 22 French, respectively) that elicited over twenty thousand replies (more on this in §2). The geolinguistic distribution of N1 and N2 is quite regular, as shown in Fig. 1 , which overviews the distribution of dialects that exhibit N1 (purple dots), N1 and N2 (green dots), and N2 (yellow dots) in the 1. 046 ALF/AIS datapoints. The map in Fig. 1  plots the data from a couple of AIS/ALF maps, both reporting translations of negative declarative clauses. Besides geolinguistic variation, however, N1/N2 vary along several other dimensions, both external (sociolinguistic) and internal.
Several works have dwelt on sociolinguistic variation in negation marking, especially in French. Ashby's 1981 seminal paper, for instance, examined a corpus of over 100 interviews in colloquial French recorded in the Tour region, showing that the occurrence of N1 varies according to a complex set of linguistic, stylistic, and social factors, including discourse setting (formal/informal), age, social class, etc. The AIS/ALF dataset, however, is not amenable to sociolinguistic analysis of the kind that Ashby did because both AIS and ALF interviews were conducted with a single speaker per dialect and informants were all NORM speakers (Nonmobile, Old, Rural Men) in order to minimize diastratic and diaphasic variation. However, since various kinds of negative clauses are contained in both AIS and ALF questionnaire, we can try to compare the occurrence of N1 and N2 in different syntactic environments, including some of those that-according to previous studies such as Ashby 1981, Pescarini & Donzelli 2017-may affect the behavior of N1 and N2.
Ashby's study showed that the incidence of N1 is affected by internal-i. e. grammatical-factors. Ashby shows that N1 is found less frequently in sentences containing a NPI, in embedded clauses (especially in subjunctive clauses), and when a subject clitic is present. Similar results are found when, instead of comparing speakers of a single variety, we compare data from nearby dialects. Pescarini & Donzelli 2017, for instance, found that variation in a sample of Ticinese dialects (Lombard-type dialects spoken in southern Switzerland) appears to be related to syntactic factors such as mood or the adjunct/argument status of NPIs, although their results need to be confirmed on a bigger dataset like the one examined in this study. If variation across sentences-i. e. across AIS/ALF maps-is examined, cross-linguistic variation appears to be more nuanced, as shown in Fig.
2. While Fig. 1  shows the distribution of N1 and N2 in indicative clauses, Fig. 2  was generated by superimposing the data from various contexts, e. g. imperatives, questions, if clauses, etc. The light green and dark yellow points in Fig. 2  signal that, in certain datapoints, the distribution of N1 and N2 varies across syntactic environments. To find out whether cross-linguistic and cross-contextual variation correlates with grammatical factors, we need to perform a multivariate analysis of the AIS/ALF dataset with the intent of verifying whether the distribution of two dependent variables such as the incidence of N1 and N2 is linked to other syntactic factors or not. The main concern with this methodology is that syntactic factors are intertwined as sentences are often characterised by multiple grammatical factors at the same time.
Section 6 concludes. In order to minimize random errors, the distribution of N1 was conducted on a subset of 19. 432 sentences (see Appendix xxx). It is worth recalling that the primary data analysed in this work have been collected and transcribed almost a century ago. The mapping from primary data to metadata has been carried out manually in order to ensure accuracy, but some data remained unclear and were therefore excluded. For instance, in examining the distribution of N1, I excluded some sentences containing other preverbal nasal formatives such as first person plural subject clitics, impersonal clitics deriving from Latin HOMO 'man' and partitive/genitive clitics, e. g. ALF 97: (Des pommes) nous n'en aurons (guère). All these elements have a nasal formative that can be easily mistaken for a preverbal negation marker and, if a single nasal segment occurs in the transcription, annotators were not always able to conclude whether that segment was a negation formative or not.
Materials and Methods Table  2  shows how negation systems (N1, discontinuous: N1+N2, N2) are distributed in the two datasets (sentences containing NPIs are excluded because N2 is often in complementary distribution with NPIs). For the sake of consistency, I will focus on a subset of the AIS data, those spoken in 'northwestern' regions  (Piedmont, Lombardy, Valle d'Aosta, Liguria, Emilia-Romagna, Trentino) , thus excluding most dialects that exhibit only N1. Even if we focus on northwestern Italo-Romance dialects, yet the co-occurrence of N1 and N2 in the two datasets remains uneven because patterns of discontinuous negation are quite frequent in the ALF, as witnessed by the greenish dots in Fig. 2 , but relatively rare in the AIS. The data reported in the AIS and the ALF have been collected following a well-established fieldwork methodology in dialectology: questionnaire-based interviews with NORM (Non-mobile, Old, Rural Men) informants, carried out in the late 19 th and early 20 th century (respectively, for the ALF and the AIS).
Interviews were conducted by experienced linguists, who were specifically selected and trained for the purposes of the project. All data were transcribed on the spot by using ad hoc phonetic alphabets. The AIS/ALF questionnaires consist of thousands of items (words, phrases or sentences). Each interview took several hours, usually divided in multiple sessions across three days. Interviewers had therefore the opportunity to interact with their informant and with other members of the same community for a relatively long time span. By cross-checking the elicited material with spontaneous speech (and with data from the nearby dialects surveyed during the same campaign), AIS/ALF interviewers were able to provide a faithful picture of each dialects. In principle, mixed methodologies (Poletto and Cornips 2005) would probably elicit better results, in particular with respect to a phenomenon such as negation marking that, as mentioned in Section 1, is subject to sociolinguistic, stylistic and contextual variation.
However, with atlases such as the AIS or the ALF you must "make the best of your incomplete data" (Garzonio & Poletto 2018), while accepting the limits of tools designed more than a century ago. A quantitative perspective is, in my opinion, the safest way to revive the data contained in atlases such as the AIS and the ALF by limiting possible biases that are intrinsic to traditional dialectological enterprises. The data contained in the above sources have been first mapped into discrete (binary) variables and factors, which have been organized in a single spreadsheet. Supervised annotators worked mainly on the original maps of the AIS and ALF, which had already been digitized and can be freely downloaded or consulted on line. By looking at areal distributions, annotators had better chances to understand the phenomena under study, provide a more precise segmentation of the transcribed material (sometimes phonetic transcriptions can blur morphosyntactic structure), and, in the end, ensure a more correct annotation of the data.
Each annotation was crosschecked by another annotator and, eventually, by myself. We obtained a single spreadsheet containing three kinds of metadata: 1) Source: AIS/ALF, sentence and datapoint identifiers, geographical coordinates, region/province; 2) Dependent variables: presence/absence of N1 and N2; 3) Independent variables, e. g. clause type, mood, presence and type of a co-occurring NPI. Clause typing was coded following a scale of features: force > mood/modality > tense/aspect. Declarative, indicative, perfect clauses were considered baseline values; to tag the remaining sentences, annotators chose the highest feature in the scale that differs from the baseline (for instance, all interrogatives have been tagged as 'question' regardless of their tense/aspect/mood specifications). A screenshot of the dataset-which can be freely downloaded from XXX-is shown in Fig.
3 : The statistical analysis has been carried out by using the r package Rbrul (Johnson 2009). The software performs multiple logistic regression in order to assess how independent variables affect the distribution of dependent variables, i. e. the incidence of N1 and N2. Rbrul is the last descendant of the family of variable rule programs, which sociolinguists have been using extensively since the early 70's to evaluate the effects of social and linguistic factors on a binary variable. Rbrul, like its predecessors, identifies which groups of factors significantly affect the chosen dependent variable and, for each group of factors (e. g. age, clause type, phonological context), it weights to what degree specific factors affect the distribution of the independent variable. Since Rbrul cannot handle politomous variables, each possible negation pattern (N1, N1&N2, N2) was reduced to a combination of two binary variables  (10, 11, 01).
Imperatives are still awaiting to be encoded in the dataset. As for indefinite and partitive objects, the dataset does not contain enough data. For reasons of feasibility, then, only one external factor (Region) and two grammatical factors (Clause typing and Type of NPI) will be tested throughout Sections 3-5. The effect of Person/number will be briefly discussed in Section 3. The list of factors and example of specific tags/values are given in Table  3.
Negation marking in Italo-Romance (AIS) As shown in Fig. 1 , N2 is attested in northwestern regions such as Valle d'Aosta, Piedmont, Lombardy, southern Switzerland (where both Lombard and Rhaeto-Romance dialects are spoken), Emilia-Romagna, and in two southern datapoints where northern communities emigrated in the Middle Ages. Some instances of N2 are attested in Trentino and Liguria, whereas the eastern regions such as Friuli and Veneto-like all central and southern varieties-have a (predominant) N1 system. I therefore focused on the 191 AIS datapoints that belong to the former group of regions in order to obtain more robust statistical results (the relevant subset of dialects can be selected by using the 'northwestern' tag in the dataset). The two following subsections focus on N1 and N2, respectively.
N1 (AIS) As previously mentioned in Section 2, northern Italo-Romance is not the most appropriate test bed to study the evolution across the various stages of Jespersen's cycle because the sample of dialects exhibiting discontinuous negation is quite narrow. A second issue with the AIS dataset (in fact, an issue with all linguistic atlases) is collinearity, i. e. too many tokens exhibit the same independent variables. To avoid collinearity (or, at least, to limit its effect), let us focus on Table  4 , which illustrates the structure of the dataset with respect to our two main grammatical factors: Clause and NPI. Table  4  shows that most indicative sentences contain NPIs, whereas non-indicative clauses either do not contain a NPI or contain a type of NPI that is not present in indicative clauses.
If I tried to test models including both factors-NPI Type and Clause-the statistical analysis would return unfaithful results because the data are not well balanced. I therefore decided to test two separate subsets of data (corresponding to the grey cells in Table  4 ): a) indicative clauses containing NPIs and b) all clauses containing no NPI. For each subset of data, I tested mixed models containing both geolinguistic and grammatical factors to find out whether Region alone accounts for the data or whether mixed models including external and grammatical factors have a better predictive power. I found that Region is always a significant factor, as expected in a dataset containing data from tightly related languages. For this reason, the results concerning the factor Region will be systematically omitted from now on.
g. Ven. no sta partir 'do not leave', lit. 'not stay to leave'), a subjunctive form or an infinitive. Zanuttini (1997: 105-107) claims that suppletive imperatives are found in dialects without N2, while in dialects in which N2 is available, negative and positive imperative may have the same form (see Garzonio & Poletto 2018:5 for apparent counterexamples and discussion). At present, I cannot verify whether Zanuttini's generalization is confirmed or not by the AIS/ALF dataset because the data on imperatives still await to be coded in our spreadsheet. However, without a clear indication about the incidence of suppletive imperatives, it seems to me that the comparison between imperatives vs other clauses is not trustworthy. Another issue with imperatives is that they cannot exhibit subject clitics, which-according to previous corpus studies on French-may play a role in the retention of N1 (Ashby 1981).
As previously mentioned, these issues will remain open until a finegrained analysis of subject clitics in the AIS datapoints will be carried out. I then tested how NPIs affect the distribution of N1 in the AIS dataset. The data in Tab. 7 show the frequency and probability of N1 with respect to various classes of NPIs and in negative sentences without NPIs. Consider that the following probability rates were obtained in a model containing factors Region and NPI Type; only the subset of indicative clauses was taken into consideration to avoid collinearity (cf. Table  4 ). Both factors (Region and NPI Type) proved significant. If we take negative sentences lacking NPIs as baseline, we find that, on average, NPIs tend to favour the retention of N1, although the difference between clauses containing and not containing NPIs is quite narrow.
This explains why the occurrence of N1 in clauses containing 'yet' is less frequent/probable than in other negative clauses. After having removed sentences containing 'yet', I grouped the values 'anymore', 'anything', 'never', and 'anywhere PP' together, thus obtaining a binary factor 'presence' vs 'absence of NPI', which proved not to be statistically significant in a model containing factors Region (p=2. 1e-170) and NPI Type (p=0. 015) (as above, the model was tested on the subset of indicative clauses). This amounts to saying that the presence of a generic NPIs is not predictive of the distribution of N1 in the AIS dataset. Specific NPIs, however, are better predictors of the behaviour of N1, which is more likely retained when the NPI is embedded in an adjunct PP. Before addressing N2 (cf.
Person seems to play a role in the distribution of N1 and I briefly commented on the possible morphophonological and syntactic reasons that might link Person and negation marking in dialects with subject clitics. The role of subject clitics, however, remains open to further research. The absence vs presence of NPIs plays no significant role in the distribution of N1 in the AIS dataset, but N1 is retained more frequently in combinations with adjuncts NPIs. Lastly, N2 and certain NPIs (adjunct PPs and negative coordinators) can marginally co-occur in a negative concord configuration. If NPIs are licensed by N2, N1 seldom occurs.
N1 (ALF) Table  11  shows the structure of the ALF dataset with respect to factors Clause and NPI Type. As I did with the AIS dataset in Section 3. 1, I first verified whether Clause is significant in determining the distribution of N1 in sentences not containing NPIs. Then I focused on the role of NPIs in indicative and modal clauses, which occur in the dataset with and without NPIs. First of all, I tested whether clausal factors are significant in the distribution of N1. After having removed all tokens containing NPIs, I began testing models with factors Region and Clause. Both factors proved significant (Region p=~0; Clause p=3. 3e-59). Frequencies and probabilities of N1 with respect to Clause are reported in Table  12 , which shows that indicative clauses are the context in which N1 is most disfavored (the usual caveats apply for imperatives, cf.
Section 3. 1). Then I checked the role of NPIs in indicative clauses. Rbrul showed that factors Region and NPI Type are both significant in the distribution of N1 (Region p=1. 4e-260; NPI Type p=8. 9e-165). The data in Table  13  show that N1 is found more probably in sentences containing NPIs. This holds particularly true for sentences with a preverbal negative quantifier (factor: Nobody subj. ): in this context the occurrence of N1 is almost at ceiling. In this respect, it is worth noting that the condition cannot be checked in the AIS dataset, which does not contain a comparable sentence. However, even if the AIS had had cases of preverbal quantifiers, the comparison would have been far from straightforward because most Gallo-Romance are strict negative concord languages in which a preverbal NPI must co-occur with N1, while most Italo-Romance dialects are weak negative concord languages.