The function of Spanish and English relative clauses in discourse and their segmentation in Centering Theory
Bibliographic record
Abstract
This study explores the processing of English and Spanish relative clauses (RCs) in discourse.The main goal is to understand how RCs contribute to the textuality of a text and, on the basis of this understanding, to propose the most adequate method for their segmentation in Centering Theory.Centering Theory is a theory of discourse structure that models textual cohesion from one "utterance" to the next.The definition of "utterance" is thus instrumental to the application of the Centering algorithm.It is also a key step for any theory of discourse structure.To this point, there is no consensus on what the basic unit of analysis of discourse should be, though the sentence and the clause tend to be the most widely accepted proposals.An analysis of complex clauses reveals that the choice between these two segmentation categories is not always straightforward.In particular, RCs present a challenge for the discourse analyst: While they are finite clauses, they are either embedded in or dependent on another clause.In order to address this challenge, this study investigates the processing of 200 RCs selected from English and Spanish texts belonging to four different genres.It evaluates five different approaches to their segmentation following Systemic Functional Linguistics (SFL).The evaluation takes into consideration different functional properties of RCs that are associated with their restrictiveness.The adequacy of the different segmentation approaches is measured in two ways: (a) by assessing the degree with which the focus of attention is maintained from an utterance to the next, following Constraint 1 and Rule 2 of Centering Theory; and (b) by identifying the frequency of subsequent mentions of RC entities in the unfolding discourse.The results of a factorial mixed-design ANOVA show that the segmentation approach that identified independent clauses and/or finite clauses in paratactic relations as the unit of analysis had the highest scores in all measures.Based on these findings, we are able to specify the notion of "utterance" in Centering Theory at the same time as we move towards a more systematic approach to the segmentation of discourse.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".