Proposing a Customised Method for Extratextual Documentative Annotation on Written Text Corpus
Bibliographic record
Abstract
In this paper, we have made an attempt to portray a perceivable sketch of extratextual documentative annotation which, in the present frame of text annotation, is considered as one of the indispensable processes through which we can add representational information to the texts included in a written corpus. This becomes more important when a corpus is made with a large number of texts obtained from different genres and text types. To develop a workable frame for extratextual annotation, at each stage, we have broadly classified the existing processes of corpus annotation into two broad types. Moreover, we have tried to explain different layers that are embedded with extratextual annotation of texts as well as marked out the applications which can substantially enhance the accessibility of language data from a corpus for the works of text file management, information retrieval, lexical items extraction, and language processing. The techniques that we have proposed and described in this paper are unique in the sense that these are highly useful for expanding the utility of data of a written text corpus beyond the immediate horizons of language processing to the realms of theoretical, descriptive, and applied linguistics. In this paper, we have also argued that we should try to annotate all kinds of written text corpora so far developed in different natural languages at the extratextual level in a uniform manner so that the text samples stored in corpora can be uniformly used for various works of descriptive linguistics, theoretical linguistics, language technology, and applied linguistics including grammar writing, dictionary compilation, and language teaching. The annotation scheme proposed here is applied on a sample Bangla text corpus and we have noted that the accessibility of data and information from this kind of corpus is far easier than that of an un-annotated raw corpus.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.021 | 0.013 |
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".