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Record W1588239581 · doi:10.14264/158129

Acquisition of word order in Chinese as a foreign language: An error taxonomy

2006· dissertation· en· W1588239581 on OpenAlexaboutno aff
Wenying Jiang

Bibliographic record

VenueThe University of Queensland · 2006
Typedissertation
Languageen
FieldComputer Science
TopicText Readability and Simplification
Canadian institutionsnot available
Fundersnot available
KeywordsWord orderLinguisticsSecond-language acquisitionComputer scienceSentenceNatural language processingVerbChinese as a foreign languageForeign languageTaxonomy (biology)Artificial intelligence

Abstract

fetched live from OpenAlex

Research in the field of Chinese second/foreign language (L2) acquisition, at present, does not match the increasing demand to learn Chinese as an L2, given that Chinese is the fastest growing foreign language (FL) in countries such as Japan, South Korea, the United States, Canada and Australia. There is a significant gap between Chinese L2 acquisition research and the large body of literature in second language acquisition (SLA), which mainly focuses on English L2. The need for more research in Chinese SLA is compelling.Particularly, research in Chinese L2 word order acquisition requires more attention because word order plays a more complex role in Chinese than in English. Chinese relies heavily on word order for information structuring of a sentence because this language lacks other means, such as verb endings indicating tense and aspect, to accomplish this function. Due to the different roles word order plays in Chinese and English, adult English-speaking learners find Chinese word order acquisition very challenging. Chinese L2 word order errors frequently occur in learners' L2 production. However, Chinese L2 researchers and teachers are left with no means to adequately describe and explain these errors for instruction purposes. This dissertation develops such a means -- a comprehensive taxonomy of Chinese L2 word order errors. This taxonomy organizes these errors into a logical system of classification. Through the classification, explicit description of various Chinese L2 word order errors is achieved, and specific sources of these errors are traced.Data was collected from 116 native-English-speaking learners of Chinese at a large university in Australia. The Chinese L2 learners were divided into three proficiency levels based on their institutional status. Four hundred and eight word order errors were extracted by qualitatively analyzing the learners’ written samples. Among the 408 word order errors, 404 (99%) are successfully classified into different categories according to a new criterion proposed in this dissertation.The new taxonomy provides a principle-based description and explanation of various Chinese L2 word order errors. A word order error is deemed to constitute an error when it violates a relevant word order principle (or sub-principle). These principles not only explain why an error is an error but also provide a means for correcting the error. In a pedagogical sense, the directness and explicitness in explaining word order errors achieved by employing this taxonomy cannot be achieved by relying on any other sources of errors available in the literature.The new taxonomy overcomes the limitations of existing taxonomies in the literature that are either superficial, or unsystematic, or not empirically testable. For example, it draws on the Cognitive Functionalist Approach of L2 acquisition. Both its description and explanation of Chinese L2 word errors go beyond superficiality. The approach maintains that adult L2 learners' conceptualization of the world is initially based on their L1. Their conceptualization of the world imposes constraints on the linguistic structures of their L2. Therefore, errors may occur when English learners of Chinese impose their conceptualization based on the English language onto the Chinese structures. The new taxonomy is systematic because it categorizes word order errors using one criterion. New categories emerging from the data and the existing categories from the literature are incorporated into one system. Finally, the new taxonomy is empirically testable because many new categories emerged from the data. It is an open-ended rather than a closed system. New categories can be added as necessary.The dissertation finds that violation of relevant word order principles has a high explanatory value for the various word order errors encountered in the data. This has clear pedagogical implications. Chinese L2 learners generally lack awareness of the word order principles (and sub-principles) on which the new taxonomy is based. These principles and sub-principles are seen to be of considerable importance to the acquisition of Chinese L2 word order. In order to improve learners' word order performance, the results of this study indicate that it is imperative for the basic Chinese word order principles be included in a CFL curriculum.

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.039
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.039
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0070.006
Science and technology studies0.0020.003
Scholarly communication0.0020.003
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.011
GPT teacher head0.238
Teacher spread0.227 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations2
Published2006
Admission routes1
Has abstractyes

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