Traditional Approaches to Etymological Data ● traditional etymology looks back on great success story ● huge dictionaries have been published (Pokorny 1959, Kluge 1883, Mayerhofer 1986–2001, Meyer-Luebke 1911) ● thousands of word histories have been reconstructed “Chaque mot a son histoire!” (attr. to Jules Gillieron, 1854-1926)
Drawbacks of Traditional Approaches Etymological dictionaries are: 1. extremely time consuming to produce and to use 2. insufficiently formalized, untransparent, and idiosyncratic 3. difficult if not unrealistic to produce for understudied languages
Quantitative Approaches to Etymological Data Current approaches to quantitative historical linguistics: ● rely on wordlists of basic vocabulary (Atkinson & Gray 2006) ● show an increase in breadth (more languages) ● show a decrease in depth (fewer words per language) ● usually ignore morphology (important in traditional approaches) ● show an untransparent motivation for cognate judgements ● usually never reach the etymological dictionary level of annotation
Challenges for Computers “the belly” in Chinese and Tibetan: ● Old Chinese *puk ● Written Tibetan (1) grod-pa ● Written Tibetan (2) gsus-pa ● Lhasa Tibetan [tʂʰo¹³-ko ²]
Challenges for Computers “the belly” in Chinese and Tibetan: ● Old Chinese *puk ● Written Tibetan (1) grod-pa ● Written Tibetan (2) gsus-pa ● Lhasa Tibetan [tʂʰo¹³-ko ²]
Challenges for Computers “the belly” in Chinese and Tibetan: ● Old Chinese *puk ● Written Tibetan (1) grod-pa ● Written Tibetan (2) gsus-pa ● Lhasa Tibetan [tʂʰo¹³-ko ²]
Challenges for Humans “There is a severe imbalance of being data-rich and theory-poor.” (William S.-Y. Wang, 1996) ● many datasets on South-East Asian languages have been published (Sidwell 2015, Wang 2004, Huang 1992, etc.) ● large digitized collections have been made available via the STEDT project (Matisoff 2011) ● but the majority of these data is unprocessed (not further checked by linguists), lacking etymologies, cognate judgments, phonetic transcriptions, or concept annotations
The Best of Two Worlds? Can we combine the advantages of traditional and quantitative approaches to profit from computational efficiency and human insight? Which challenges do we face when pursuing integrated frameworks in South-East Asian languages?
Etymological Database of Burmish Languages Background: ● part of ERC synergy grant 'Beyond Boundaries' (SOAS, British Museum, British Museum) Goal: ● creating a classical etymological dictionary, taking full advantage of computational approaches with an openly published database online
Previous Research Burlig (1967): pioneering and rigorous, but data too sparse Bradley (1979): inexplicit, not Burmish (Loloish) Mann (1998): no use of Old Burmese, no relative chronology of changes, morphemes as cognate sets Nishi (1999): no reconstruction, very clean organization into cognate sets, larger dataset than predecessors
Challenges: Suprasegmental Correspondences Not all sound correspondences occur between sounds which are in the same prosodic position of a word. Notably processes like tonogenesis and various patterns of aspiration and voicing often co-occur with other sound changes. In these cases, a simple alignment of the words under consideration is usually not enough, but an analysis of the patterns of sound correspondences needs to be carried out.
Challenges: Partial Cognates ‘Cognacy is not a binary relation which is either present or not. Instead, we can distinguish different subtypes of cognacy, just as biologists can identify specific types of homology between genes.’ (List 2016: 133) partial cognacy in Chinese dialects (List et al. 2016)
Challenges: Partial Cognates Binarisation of Partial Cognate Relations: ● strict (only fully identical words are considered cognate) ● loose (words sharing a cognate morpheme are cognate) Problems of Binarisation: ● not realistic with respect to lexical change ● over- or underestimates the amount of shared cognates
Challenges: Language-Internal Cognates When using alignments to derive statistics from sound correspondences, dependencies inside a language need to be taken into account to avoid an overscoring of regularities. Language-internal cognates are invaluable evidence in classical cognate judgments and reconstruction. Current computational approaches ignore them completely.
Challenges: Language-Internal Cognates prefix a- in Old Burmese ● “the branch” a khak ● “the mother” a miy ● “the flower” a po₁ṅʔ ● “the feather” a muyḥ ● “the father” a phiy ● “the leaf” a ro₁k “the dog” in Atsi ● “the wolf” [vam ¹kʰui²¹mo ] ● “the dog” [kʰui²¹] ● “the fox” [tan kʰui²¹]
Materials ● data taken from Huang (1992) ● currently 8 Burmish varieties ● 248 concepts selected (basic vocabulary, and etymologically important words) ● partial cognates were automatically inferred and then manually corrected ● alignments were automatically computed (will be manually corrected)
Sound Correspondences: Searching for Patterns What shall we do with morpheme alignments? ● if the Neogrammarians are right, ○ a given proto-form in a given context should always yield the same reflex in a given descendant language ● this means, ○ compatible patterns in aligned cognate sets will hint to specific proto-sounds or proto-sounds in specific contexts
Sound Correspondences: Searching for Patterns What is compatibility? ● take all alignments for a given dataset, and select one common sound position (e.g., initial of each morpheme) ● when plotting for each language in our sample, which sound occurs in a given cognate set in the position, we can make a first step to compare these patterns
Sound Correspondences: Searching for Patterns What is compatibility? compatible Cognate set L1 L2 L3 L4 L5 L6 L7 L8 morpheus-1 p p p Ø f f Ø p morpheus-2 p Ø p p Ø f p p
Sound Correspondences: Searching for Patterns What is compatibility? compatible Cognate set L1 L2 L3 L4 L5 L6 L7 L8 morpheus-1 p p p Ø f f Ø p morpheus-3 Ø p p p f f p p
Sound Correspondences: Searching for Patterns What is compatibility? NOT COMPATIBLE Cognate set L1 L2 L3 L4 L5 L6 L7 L8 morpheus-1 p p p Ø f f Ø p morpheus-4 Ø p f p f p p p
Sound Correspondences: Searching for Patterns What is compatibility? ● compatibility of two identical positions in different alignments is a necessary requirement to assume that the two alignments represent a common proto-sound in a common proto-context ● it is not sufficient, as we have to deal with missing data, which may sufficiently blur the picture
Sound Correspondences: Searching for Patterns Building a compatibility network of aligned cognate sets: ● take the same position (e.g., initial consonant) in all alignments (called a “site” of the alignment) ● make a network in which the alignment sites are nodes ● edges in the network are drawn between two nodes if these are compatible with each other ● weights between the edges are determined by counting the positions without a gap in both alignment sites
Sound Correspondences: Searching for Patterns Search for maximal cliques to increase cluster compatibility: ● A clique in a network is a group of nodes which are all connected with each other. ● Cliques of compatible alignment sites represent the strongest evidence for a coherent group of regular correspondences pointing to the same proto-sound in a given context. ● We use a simple method to search for non-overlapping cliques by maximizing their size, which is done in an iterative manner.
Sound Correspondences: Searching for Patterns Search for maximal cliques to increase cluster compatibility: ● What looks chaotic is less scary, if we look at the patterns! ● Of 638 cognate sets: ○ 337 occur in at least 3 taxa ○ 317 start with an initial consonant ○ 234 could be assigned to 35 transitive groups of minimal size 2 ○ 74% (234 / 337) of the cognate sets can be seen as “regular”. The remaining cognate sets will be checked and either corrected or their incompatibility will be explained.
Sound Correspondences: Searching for Patterns Be careful with the interpretation of compatibility networks: ● remember, one clique in our network does not necessarily correspond to one proto-sound in the proto-language (maybe, our alignments are wrong, our cognates are wrong, the words are borrowed…) ● but: if a proto-sound in a certain number of identical contexts has derived regularly from proto-language to descendant languages, it should form a clique in our data! ● compatibility networks of prosodically similar alignment sites are just a first step towards computer-assisted language reconstruction
Language-Internal Cognates: Improving Annotation Atsi ● “the moon” [lo ̱ mo ] ● “the tiger” [lo²¹mo ] ● “the wolf” [vam ¹kʰui²¹mo ] ● “the dog” [kʰui²¹] ● “the fox” [tan kʰui²¹] Bola ● “the wolf” [mjaŋ kʰui³ ] ● “the dog” [kʰui³ ] ● “the thunder” [mau³¹mjaŋ ] ● “the sky” [mau³¹kʰauŋ ]
Language-Internal Cognates: Improving Annotation Language Concept Word Motivation Atsi the moon lo ̱ mo moon m-suffix Atsi the tiger lo²¹mo tiger m-suffix Atsi the wolf vam ¹kʰui²¹mo bear dog m-suffix Atsi the dog kʰui²¹ dog Atsi the fox tan kʰui²¹ fox(?) dog
Language-Internal Cognates: Improving Annotation Lexeme motivation annotation in the EDICTOR: ● simplified schema, all glosses are allowed, as long they do not contain white-space ● question marks can be used to express doubt ● identically annotated morphemes can be inspected (they indicated language-internal cognates) ● partial and full colexifications can be used to assist the morphological analysis ● data can be visualized with help of partial colexification graphs
Outlook Chances are there! ● working with partial cognates increases the realism of our analyses ● working with alignments opens new horizons for computer-assisted consistency analysis ● annotation of language-internal cognacy opens exciting research avenues for the investigation of semantic change, lexical typology, and language relationship
Outlook Challenges remain! ● How can we get from compatibility clusters in our alignments to first reconstructions? ● Can we use compatibility to test the consistency of reconstruction systems? ● How can we formalize the assignment of cross-semantic cognates in our wordlists?
Thanks for Your Attention! Thanks to: ● the Équipe AIRE (UPMC, Paris) for inspiration and help with network analyses ● Guillaume Jacques and Laurent Sagart for helpful feedback on EDICTOR tool and active help in creating the first concept list ● Doug Cooper for helping out with parts of the data that we used
References Bradley, David (1979). Proto-Loloish. London: Curzon Press. Burling, Robbins (1967). Proto-Lolo-Burmese. Bloomington: Indiana University. Hill, Nathan W. (2013) 'The merger of Proto-Burmish *ts and *č in Burmese.' SOAS Working Papers in Linguistics 16: 334-345. Kluge, Friedrich (1883). Etymologische Wörterbuch der deutschen Sprache. Strassburg: K. J. Trübner. List, Johann-Mattis (2014): Sequence comparison in historical linguistics. Düsseldorf: Düsseldorf University Press.
References List, Johann-Mattis, Philippe Lopez, and Eric Bapteste (2016): Using sequence similarity networks to identify partial cognates in multilingual wordlists. Proceedings of the Annual Meeting of the ACL. Berlin: Association of Computational Linguistics. 599–605. Mann, Noel Walter (1998). A phonological reconstruction of Proto Northern Burmic. Unpublished thesis. Arlington: The University of Texas. Mayerhofer, Manfred (1986-2001). Etymologisches Wörterbuch des Altindoarischen. Heidelberg: Carl Winter.
References Meyer-Luebke, Wilhelm (1911). Romanisches etymologisches Wörterbuch. Heidelberg: Winter. Nishi, Yoshio (1999). Four Papers on Burmese: Toward the history of Burmese (the Myanmar language). Tokyo: Institute for the study of languages and cultures of Asia and Africa, Tokyo University of Foreign Studies. Pokorny, Julius (1959). Indogermanisches etymologisches Wörterbuch. Bern and Münich: Francke.
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