MétaCan
Menu
Back to cohort

Morphology in Dene-Yeniseian Languages

2019· reference-entry· en· W2963590687 on OpenAlexaboutno aff
Edward J. Vajda

Bibliographic record

VenueOxford Research Encyclopedia of Linguistics · 2019
Typereference-entry
Languageen
FieldComputer Science
TopicNatural Language Processing Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsPrefixVerbLinguisticsMorphemeLanguage familySuffixHistoryPhilosophy

Abstract

fetched live from OpenAlex

Abstract Dene-Yeniseian is a putative family consisting of two branches: Yeniseian in central Siberia and Na-Dene (Tlingit-Eyak-Athabaskan) in northwestern North America. Yeniseian contains a single living representative, Ket, as well as the extinct Yugh, Kott, Assan, Arin, and Pumpokol languages. Na-Dene contains Tlingit, spoken mainly in the Alaskan Panhandle, and a second branch divided equidistantly between the recently extinct Eyak language of coastal Alaska and the widespread Athabaskan subfamily, which originally contained more than 40 distinct languages, some now extinct. Athabaskan was once spoken throughout interior Alaska (Dena’ina, Koyukon) and most of northwestern Canada (Slave, Witsuwit’en, Tsuut’ina), with enclaves in California (Hupa), Oregon (Tolowa), Washington (Kwalhioqua-Clatskanie), and the American Southwest (Navajo, Apache). Both families are typologically unusual in having a strongly prefixing verb and nominal possessive prefixes, but postpositions rather than prepositions. The finite verb arose from the amalgamation of an auxiliary and a main verb, both with its own agreement prefixes and tense-mood-aspect suffixes, creating a rigid, mostly prefixing template. The word-final suffixes largely elided in Yeniseian but merged with the ancient verb root in Na-Dene to create a series of allophones called stem sets. Na-Dene innovated a unique complex of verb prefixes called “classifiers” on the basis of certain inherited agreement and tense-mood-aspect markers; all of these morphemes have cognates in Yeniseian, where they did not innovate into a single complex. Metathesis and reanalysis of old morphological material is quite prevalent in the most ancient core verb morphology of both families, while new prefixal or suffixal slots added onto the verb’s periphery represent innovations that distinguish the individual daughter branches within each family. Other shared Dene-Yeniseian morphology includes possessive constructions, directional words, and an intricate formula for deriving action nominals from finite verb stems. Yeniseian languages have been strongly affected by the exclusively suffixing languages brought north to Siberia by reindeer breeders during the past two millennia. In modern Ket the originally prefixing verb has largely become suffixing, and possessive prefixes have evolved into clitics that prefer to attach to any available preceding word. Na-Dene languages were likewise influenced by traits prevalent across the Americas. Athabaskan, for example, developed a system of obviation in third-person agreement marking and elaborated an array of distinct verb forms reflecting the shape, animacy, number, or consistency of transitive object or intransitive subject. Features motivated by language contact differ between Tlingit, Eyak, and Athabaskan, suggesting they arose after the breakup of Na-Dene, as the various branches spread across northwestern North America. The study of Dene-Yeniseian morphology contributes to historical-comparative linguistics, contact linguistics, and also to the diachronic study of complex morphology. In particular, comparing Yeniseian and Na-Dene verb structure reveals the prominence of metathesis and reanalysis in processes of language change. Dene-Yeniseian is noteworthy not only for its wide geographic spread and for the effects of language contact on each separate family, but also for the opportunity to trace the evolution of uncommon morphological structures.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.012
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.836
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0040.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.041
GPT teacher head0.375
Teacher spread0.334 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueOxford Research Encyclopedia of LinguisticsSame topicNatural Language Processing TechniquesFrench-language works237,207