dataesr/scientific-paragraphs-categorization huggingface dataset:
On Timor, three tap languages (Makalero, Makasae and Fataluku) are spoken in contiguous areas in the east of the island, one (Oirata) on Kisar island off the eastern tip of Timor, adjacent to an Austronesian language, and one (Bunak) in the centre of the island, surrounded by Austronesian languages. The recent publication of the online database LexiRumah  (Kaiping, Edwards & Klamer 2019)  containing lexical data for 357 language varieties spoken in eastern Indonesia and Timor-Leste enables a comparison of lexical data that was previously impossible. In addition, recent years have seen publications of grammar descriptions and historical reconstructions of tap languages (see the overviews in as well as reconstructions of Austronesian language groups of the Flores-Lembata region  (Fricke 2019 ) and the Timor region  (Edwards 2021) ).
Thus we are now in the position to examine the contact history in the region more closely. Is there lexical evidence that there was contact between speak-klamer ers of tap with Austronesian languages? Which languages or regions were the donors, and which were the recipients of lexical and grammatical features? Can we use the evidence to reconstruct stages or regions of contact? In this chapter, I focus on traces of Austronesian words attested in the lexicon compiled for the tap languages, as present in the online lexical database LexiRumah 3. 0. 0. ; see also below. (tap borrowings ending up in Austronesian languages of the region are discussed in the chapters by  Moro et al. (this volume)  and  Schapper & Huber (this volume) ). In addition, I only focus on ancient and pre-modern borrowings.
Present Study: Methods and Materials Almost all the lexical data discussed in this paper has been drawn from primary sources compiled and referenced in the online lexical database LexiRumah 3. 0. 0  (Kaiping et al. 2019). Where other sources were used, these are provided in the text. This study investigated the vocabulary of 109 lects (i. e. language varieties or dialects) spoken on the islands of Timor, Alor, Pantar, Flores and Lembata: 54 lects belonging to the Timor-Alor-Pantar family and 55 lects belonging to the Malayo-Polynesian subgroup of Austronesian languages. To find tap lexeme sets that contained Austronesian borrowings, I first went on a fishing expedition in LexiRumah, considering lexeme sets for 75 preselected concepts in the semantic domains (taken from  Haspelmath and Tadmor 2009) : Social and political relations, Agriculture and vegetation, The house, Clothing and grooming, Food and drink, Warfare and hunting, Animals, Kinship, The physical world, and The body.
I selected 13 concepts from the following semantic fields: Social and political relations: 'king/ruler' , 'slave'; Agriculture and vegetation: 'maize' , 'seed'; Clothing and grooming: 'needle' , 'to weave' , 'sew'; Food and drink: 'salt'; Animals: 'pig' , 'deer'; Kinship 'bride price'; and The body: 'navel' , 'breast' , 'skin'. The sets discussed in this paper are not an exhaustive listing of the borrowings attested; for reasons of space, some tap lexeme sets with mp loans are left for future analysis. To proof that a lexeme set was borrowed into tap languages, it must be demonstrably Austronesian; that is, there must be a Proto Austronesian (pan) or Proto Malayo-Polynesian (pmp) reconstructed form that has a similar form and meaning. For this evidence I drew on the etymological database by  Blust and Trussel (n.
This analysis is based on a database of 1,173 reconstructions to Proto Rote-Meto (prm) or one of its daughter nodes, published as Edwards (  2021 ). 1 I investigate three areas of prm: segmental inventory, lexicon, and regularity of sound change. In §2 I examine the segmental inventory of prm. I show that when compared with Proto Malayo-Polynesian (pmp), the segmental inventory of prm has been transformed according to regional norms. Furthermore, certain prm segments are disproportionally represented in words not known to be inherited from pmp, and certain other segments are over-represented in pmp inheritances. This indicates that the transformation of the prm segmental inventory mainly occurred due to acquisition of new words with these segments. In §3 I examine the lexicon of prm. I find that prm has an entirely expected lexical profile for an Austronesian (an) language.
1. " oed Online. Oxford University Press, September 2020. Web. 3 December 2020. ) 8 Amarasi babaʔ may be from pmp *baba, of which  Blust and Trussel (2020)  state: "As part of universal nursery language this item could have arisen independently in all or many of the languages in which it appears. […] *baba is just as likely to be an inherited form which resisted regular sound change […] as a result of the recurrent reinforcement that nursery language provided. " Meto babaʔ has no known cognates in the Rote languages, and is included in the regional stratum in my database. 9 Two examples of reconstructions involving derivation are *[q/k]umaŋ > *saŋɡuma 'hermit crab' and *bañən > *kesufani 'sneeze'. Both were placed in the an stratum, even though the origins of initial *saŋɡ and *kesu are currently unknown.
There are 25 reconstructions in my database which are potentially onomatopoeic or nursery language. Of these, five are inherited from pmp,19 twelve belong to the regional stratum,20 and eight belong to the west Timor stratum. 21 These potential cases of coinage are excluded from the analysis throughout the remainder of this section. This leaves 628 reconstructions not known to be inherited from pmp which were probably acquired by language contact. These figures are summarised in Table  4. 9. In this section I investigate the likely borrowings from two perspectives: representation in basic vocabulary ( §3. 1), and distribution in different semantic spheres ( §3. 2). Unlike the segmental inventory, prm has an expected lexical profile for an an language. mp inheritances are better represented in basic vocabulary and semantic spheres more resistant to borrowing, while the other strata are more poorly represented in such areas.
3. 2 Semantic Fields An additional perspective on the composition of the prm lexicon could be provided by semantic fields. I assigned each reconstruction in my database to one of eighteen semantic fields. These semantic fields and the number of reconstructions within each are summarised in Table  4. 11. Fields are arranged from most borrowing-resistant to least borrowing-resistant, following the analysis of  Tadmor et al. (2012:41f. )  who determined borrowability on the basis of 41 languages from around the world. The semantic fields in  Tadmor et al. (2012)  follow those used by  Buck (1949)  and  Key and Comrie (2015) , which I modified in some minor ways. 23 Although these semantic fields are problematic in several respects, they have two advantages. Firstly, they allowed assignment of words to semantic fields in a neutral way to avoid confirmation bias, and secondly the resistance to borrowing of each field has been tested by  Tadmor et al.
https://creativecommons. org/licenses/by-nc-nd/4. 0 edwards In the absence of any identifiable source language, both hypotheses-borrowing or irregular sound change-are equally ad-hoc. 25 As a result, I included such words in my database, made a prm reconstruction and tracked the irregular sound changes required to derive the modern day words from the prm reconstruction. When we examine the extent to which prm reconstructions show irregularities according to strata,26 we find 19% (99/526) of inheritances from pmp have at least one irregularity in the daughter languages, 20 % (68/340) of words in the regional stratum have irregularities, and 26 % (81/307) of reconstructions in the west Timor stratum have irregularities. If it is correct that a cognate set which spreads after the break-up of the proto language is more likely to display irregular correspondences, then the larger number of irregularities in the west Timor stratum indicates that it contains more such words than the other strata.
This chapter is structured as follows. Section 2 introduces the Flores-Lembata languages and their linguistic context. Section 3 describes the dataset and methodology used to investigate the Flores-Lembata lexicon. Section 4 summarizes the historical phonology of Flores-Lembata, including reconstructed pfl phonemes and subgroup-defining sound changes. The results of the lexical study are presented in section 5 on the origins of the reconstructed vocabulary of pfl and in section 6 on the an and non-an components of the present-day Flores-Lembata languages. Section 7 discusses the implication of the results for the reconstruction of a possible contact scenario which led to the non-an part of the lexicon. In section 8, I provide a summary of the main conclusions of this study.
Also lexically, they are more similar to each other than to Sika and Kedang. Among the three Lamaholot groups, there is little inter-group intelligibility and within each group, various varieties are attested with differences in lexicon, phonology and grammar. For Western Lamaholot, high mutual intelligibility among the group-internal varieties is reported by  Michels (2017, 12). Generally, among Western Lamaholot varieties, mutual intelligibility decreases with increasing geographical distance. No empirical data is available on mutual intelligibility within the varieties of Eastern Lamaholot and the varieties of the Central Lamaholot group. -978-90-04-52945-8 Downloaded from Brill. com 06/03/2024 05:07:36PM via Open Access. https://creativecommons. org/licenses/by-nc-nd/4. 0.
Dataset The basis for this study are wordlists of 46 Flores-Lembata varieties accessible through the lexical database LexiRumah  (Kaiping and Klamer 2018; Kaiping, Edwards, and Klamer 2019) , originating from various sources as indicated in the database. Each wordlist contains between 200 and 600 lexical items. In total 607 different concepts of basic, as well as special, vocabulary are covered. For this study, additional information from dictionaries was added for some of the concepts. From the wordlists, over 400 lexeme sets were extracted using the online tool edictor (etymological dictionary editor) at https://digling. org/edictor/. From a lexical database, edictor creates sets of words with similar forms and meanings. The tool also aligns similar sounds within the sets which helps to discover sound correspondences.
I define a lexeme set as a set of formally similar words that appear across languages. There are two types of lexeme sets, cognate sets and similarity sets. Cognate sets trace back to a reconstructible proto form in a proto language, such as Proto Flores-Lembata (pfl), my own reconstructions, or/and Proto Malayo-Polynesian (pmp), as attested in  Blust and Trussel (2010). Similarity sets cannot (yet) be reconstructed to a common proto form but they are so similar that they must have some common history. Table  5. 2 shows two lexeme sets as examples. The set for the concept 'seven' is a cognate set which traces back to a pfl and a pmp reconstruction. For a similarity set, the form given is marked by a hashtag (#), such as #dahe-k 'near'. In my dataset of Flores-Lembata vocabulary, I establish a lexeme set if a similar form occurs in at least two of the Flores-Lembata subgroups.
Occasionally, my database contains sets based on lexemes that are only found in one Flores-Lembata subgroup but these are only considered if they go back to a pmp form. An example for this is the Sika (Hewa variety) word roun 'leaf' which traces The pfl forms in this study are my own reconstructions based on the analysis of the historical phonology of the Flores-Lembata languages (see section 4). I reconstruct a pfl form for a lexeme set if the following criteria are fulfilled. First, the sound correspondences between the reflexes in different subgroups have to be regular. Second, there are two possible conditions that lead to a pfl reconstruction: (i) If the lexeme set can be traced back to a pmp form, then the set is always reconstructed to pfl, or (ii) if no related pmp form is known, the set must be attested in at least Sika and Kedang to be reconstructed to pfl.
This means that a form that appears in only one or two Flores-Lembata subgroup and has a pmp form is always reconstructed to pfl. However, if no pmp form exists, only items that are attested in Sika and also in Kedang are reconstructed to pfl. Sika and Kedang are the two Flores-Lembata subgroups that are geographically the furthest apart and therefore, the occurrence of related forms in these two languages points to inheritance from Proto Flores-Lembata (pfl) rather than to diffusion after the split of the family. For most lexeme sets of this study, reconstructions are presented in tables throughout -978-90-04-52945-8 Downloaded from Brill. com 06/03/2024 05:07:36PM via Open Access. https://creativecommons. org/licenses/by-nc-nd/4. 0 fricke this chapter. For reflexes of these in the individual languages, please consult the LexiRumah database  (Kaiping, Edwards, and Klamer 2019)  or the appendix of Fricke (2019a).
3. 4 Analysis The wordlists and established lexeme sets (see section 3. 3) were analysed in several ways to answer the research questions listed in section 1. To answer question (1) How big are the an and the non-an components of the Proto Flores-Lembata (pfl) lexicon?, I compared the number of pfl forms which trace back to a known pmp form, thus being of an origin, with those that do not, thus being classified as non-an. 1 To answer question (2) How big are the Austronesian (an) and the non-Austronesian (non-an) components of the lexicon in individual varieties of each Flores-Lembata subgroup?, I selected, for each subgroup, the variety with most lexical data available. Each item in the wordlist was then classified as an if it traces back to a pmp form and as non-an if no known pmp is attested.
5 Proto Flores-Lembata (pfl) Reconstructions and Their Origin 5. 1 Overview Out of 210 pfl reconstructions in my dataset, about 82% (n=173), listed in section 5. 2, are of Austronesian origin, i. e. , trace back to a pmp form. Only a small number (n=37) of pfl reconstructions, listed in section 5. 3, cannot be connected to any known pmp form. Section 5. 4, summarizes and discusses the features of the pfl vocabulary. At the current stage of research, it remains unclear whether the pfl forms without known pmp source can be regarded as a non-Austronesian substrate of pfl. This is because many of these 37 forms are likely to be inherited from an earlier ancestor as similar forms are also found in other Austronesian languages of the region.
Summary and Conclusions The 210 pfl reconstructions are to a great extent of Austronesian origin, for 82% of them, there is a known pmp source. About one fifth of the pfl vocabulary remains of unknown origin. pfl, as a descendant of pmp, has thus replaced about 20% of the vocabulary for the concepts in this study since pmp times, i. e. around 4000 years ago  (Pawley 2005). When selecting only basic vocabulary forms (see Appendix for a list of basic concepts) from the sample, around 124 pfl forms remain. Out of these basic forms only 13% are not of pmp origin. This lower percentage of non-pmp vocabulary in pfl basic vocabulary compared to the whole database confirms that lexical replacement in basic vocabulary is less likely to occur than in other parts of the vocabulary. The pfl vocabulary which is not of pmp origin could be regarded as a non-Austronesian lexical substrate in pfl.
6. 2 The Lexicon of Individual Varieties In all three Lamaholot groups, only about 50% of the present-day lexicon trace back to an Austronesian source, as shown in Table  5. 8. In the sister languages, the an component is higher in Kedang with 57% an origin and again higher in Sika with 62% an origin. 18 The data in Table  5. 8 is based on one variety per subgroup, named in brackets in the table. I have not observed significant variation between the varieties of one subgroup regarding the distribution of pmp versus non-pmp vocabulary. Therefore, the varieties with the largest amount of data available were chosen. The percentage of non-an vocabulary is stable across the three Lamaholot groups, even though the size of the datasets varies. The Eastern Lamaholot dataset (n=128) is much smaller than the one of the Central group (n=333) and Western group (n=276) and contains proportionally more basic vocabulary than the larger datasets of Central and Western Lamaholot.
The non-an vocabulary covers virtually all semantic domains. There are large amounts of basic vocabulary denoting properties or verbal concepts, such as #plati/kati 'hot' in the Lamaholot subgroups or #tuʔay 'sleep' in Sika and Lamaholot. Also body part nouns are a rather big group with 22 non-an terms, out of which only 5 can be reconstructed to pfl. In addition to that there is special vocabulary in the domains of flora and fauna, such as #kobu 'crocodile' in Lamaholot and Sika or #tapu 'coconut' in Lamaholot and Kedang. In total, the database contains 19 non-an animal terms, out of which 6 are reconstructible to pfl, and 17 non-an terms in the semantic domain of plants, out of which 4 trace back to pfl. Table  5. 10 compares the Flores-Lembata languages with their closest Austronesian neighbours, the Rote-Meto languages on Timor (Amarasi and Termanu in the table), and the Central Flores languages in central Flores (Rongga, Keo, and Lio in the table).
Both of these studies focus on a small part of the basic vocabulary (a Swadesh list), which might be more resistant to borrowing that the lexicon overall. A section in the short grammar of Alorese by  Klamer (2011: 104-107)  indicates an estimated percentage of 5. 2 %, while an article by  Robinson (2015)  discussing Austronesian borrowings into ap languages and ap borrowings into Austronesian languages finds about 3. 8 % ap loans in Alorese. These numbers are surprisingly small, considering the length of contact. In this paper, we research whether the observation applies beyond the core vocabulary by extending the data to a 596-concept list, including all 13 Alorese dialects. Unlike other studies investigating Austronesian-Papuan borrowings (see among others Klamer's chapter in this volume), we did not pre-select the semantic domains to study, but investigated the entire dataset, and got the semantic domains of the loanwords inductively.
In order to detect borrowing events, an algorithm was used to sifts loanwords out of a huge lexical pool: ~600 words × 13 Alorese dialects, × 55 Austronesian languages, × 42 tap language varieties = approximately 66,000 word forms (see § 2). This pool is much larger than the dataset used in the previous research on ap borrowing in Alorese. The present chapter, thus, illustrates an innovative methodological approach to the study of loanwords which uses an algorithm for automatic lexical similarity detection to study loans across two linguistic families. In this chapter, we describe the two-step procedure that was employed and how the results compare to work that has done this manually, to answer questions such as: does the size of a dataset make a difference when we investigate relative amount of borrowing? And does the percentage of borrowings increase when we investigate a large dataset, including highly borrowable concepts, compared to when we investigate a Swadesh list? Another innovative aspect of the chapter is that this is the only study in which 13 dialects of a minority language of Indonesia are compared.
com 06/03/2024 05:07:36PM via Open Access. https://creativecommons. org/licenses/by-nc-nd/4. 0 a broader geographical perspective, relating our results to those of other studies in the present volume. This chapter is organised as follows. As a background to this study, we begin by providing some basic information on Alorese and ap languages in § 1; this is followed by §2 illustrating the research questions, the dataset, and the methodology of the present study. §3 presents the main findings, while § 4 discusses the findings and gives some concluding remarks.
The Present Study This paper investigates Alor-Pantar (ap) loanwords in Alorese looking at a large lexical dataset. Using a two-step combination of automatic pre-screening and qualitative checks, we classify as candidate loanwords in Alorese all forms that are not inherited from the ancestor language (Proto Flores-Lembata), but that are formally similar to their semantic equivalents in one or more ap language, and check them individually.
Dataset and Methodology In order to understand the patterns of loanwords in Alorese, we worked with word list data collected from field work and published sources aggregated in the online lexical database LexiRumah  (Kaiping, Edwards, & Klamer 2019). We use version 1. 0. 0 of the database. 3 The sources of the individual word lists and forms used here can be found on LexiRumah. The dataset contains between 104 and 756 forms (counting all synonyms, and counting polysemous words once for every meaning) associated to a list of 596 concepts. The concept list contains pronouns and numerals, and nouns and verbs relating to both basic human activities (e. g. , 'knife' , 'to pull' , 'to work' , 'fireplace ash'), as well as the natural and cultural world of the region (e. g. , 'sun' , 'island' , 'mountain' , 'dolphin' , 'rice ear bug' , 'chicken' , 'to plant yam' , 'to clear land by burning').
The language dataset contains 13 Alorese varieties or dialects (one given in two sources), each of which displays between 450 and 756 forms for those concepts, 55 other Austronesian languages or varieties, and 42 tap languages or varieties. For the Austronesian and tap languages, the dataset contains between 104 and 756 forms (counting all synonyms, and counting polysemous words once for every meaning) associated to the list of concepts. The first step we took was a data-mining process to discover potential loanword patterns in such a large dataset. So, we investigated lexical data from a quantitative perspective, by applying automatic lexical similarity detection. In the second step, we conducted a qualitative fine-grained analysis on the similarity sets whose patterns of distribution were compatible with borrowing event between Alorese and ap languages.
Basic actions and Technology thus includes Tools as well as Weapons, and The house. The Law and Religion and belief were combined with the Social and political relations field. Unlike Edwards, we also combined Animals and The Physical world. Approximately half of the ap loanwords occur in the three most borrowable semantic fields (Basic actions and technology, Social and political relations, and Agriculture and vegetation). In §3. 6 we will draw generalizations regarding their distribution among Alorese varieties, and their donor languages. All comparisons presented in this section were made with the tool edictor, (etymological dictionary editor) at https://digling. org/edictor/. edictor visualizes and allows to edit the cognate judgements in a lexical database. The tool also aligns similar sounds within the sets which helps to discover sound correspondences.
Discussion In the previous section, we have presented evidence for lexical borrowing from the ap languages into Alorese. After a close inspection, applying automatic lexical similarity detection, and subsequently a qualitative fine-grained analysis (see §2), we have detected 28 loanword events between Alorese and ap languages on a list of 596 items. The percentage of ap loanwords in Alorese is, thus, approximately 4. 7%, confirming previous results of  Klamer (2011)  and Robin--978-90-04-52945-8 Downloaded from Brill. com 06/03/2024 05:07:36PM via Open Access. https://creativecommons. org/licenses/by-nc-nd/4. 0 son (2015), which were based on smaller datasets. This result shows, on the one hand, that the percentage of ap loanwords in Alorese is indeed small, and on the other hand, that conducting loanword analysis on a Swadesh list, like in  Klamer (2011) , is likely to give a representative figure of the number of loanwords in a language.