Exploiting alignment in multiparallel corpora for applications in linguistics and language learning
Bibliographic record
Abstract
This thesis exploits the automatic identification of semantically corresponding units in parallel and multiparallel corpora, which is referred to as alignment.Multiparallel corpora are text collections of more than two languages that comprise reciprocal translations.The contributions of this thesis are threefold:• First, we prepare a large multiparallel corpus by adding several layers of annotation and alignment.Annotation is first performed on each language individually, while alignment is applied to two or more languages.For the latter case, we use the term multilingual alignment.We show that word alignment on parallel corpora can improve language-specific annotation by means of disambiguation.• Our second contribution consists in the development and evaluation of prototypical algorithms for multilingual alignment on both sentence and word level.As languages vary considerably with regard to how content is realized in sentences and words, multilingual alignment needs to be represented by a hierarchical structure rather than by bidirectional links as prevailing representation of bilingual alignment.• Based on our corpus, we thirdly show how word alignment in combination with different types of annotation can be employed to benefit linguists and language learners, among others.All tools developed in the context of this thesis, in particular the publicly available web applications, are driven by efficient database queries on a complex data structure.iii First of all, I wish to thank my supervisor Martin Volk who guided me through the initial troubles, gave me room to realize my own ideas and had the necessary confidence in me to finish this big project of mine.I am likewise
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.012 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.008 |
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".