<b>A rainbow of corpora:</b> Corpus linguistics and the languages of the world. Ed. by Andrew Wilson, Paul Rayson, and Tony McEnery. (Linguistics edition 40.) Munich: LINCOM Europa, 2003. Pp. 165. ISBN 3895868728. $73.20 (Hb).
Bibliographic record
Abstract
Reviewed by: A rainbow of corpora: Corpus linguistics and the languages of the world ed. by Andrew Wilson, Paul Rayson, and Tony McEnery Heiko Narrog A rainbow of corpora: Corpus linguistics and the languages of the world. Ed. by Andrew Wilson, Paul Rayson, and Tony McEnery. (Linguistics edition 40.) Munich: LINCOM Europa, 2003. Pp. 165. ISBN 3895868728. $73.20 (Hb). This volume is a collection of papers that were originally presented at Corpus Linguistics 2001, a conference held 30 March–2 April 2001 at Lancaster University (UK). The sister volume to the ‘English-oriented’ Corpus linguistics by the Lune: A festschrift for Geoffrey Leech (Frankfurt: Peter Lang, 2003), it includes contributions that are concerned with non-English languages. The languages dealt with in the present volume range from the better-known Indo-European languages to Biblical Hebrew, Korean, and Arabic. Content-wise, the individual articles can be roughly divided into three categories. First, there are three diachronically oriented papers from the workshop ‘Corpus linguistics, ancient languages, and older language periods’: Beatrix Färber on a corpus of Medieval Irish (19–26), Wolf-Dieter Syring on the design and usage of a text database of Biblical Hebrew (141–52), and Matthew Brook O’Donnell, Stanley E. Porter, and Jeffrey T. Reed on the database-assisted discourse analysis of texts from the New Testament (109–21). The other papers, which come from the main session, deal with modern languages. Half of them are primarily concerned with corpora as such that is, their design, mark-up, usage, and so on. Anne Abeillé, Lionel Clément, Alexandra Kinyon, and François Toussenel discuss the PARIS 7 annotated corpus for French (1–10), and Martin Beaudoin and Michel Samard present their work on a large corpus of written Canadian French (11–18). R. Rossini Favretti, F. Tamburini, and C. de Santis deal with the CORIS corpus of written Italian (27–38), and Eva Hajičová and Petr Sgall discuss the Prague Dependency Treebank (39–50). Shereen Khoja, Roger Garside, and Gerry Knowles present a tagset for the morphosyntactic tagging of Arabic (59–72),and Kiril Simov, Gergana Popova, and Petya Osenova introduce a HPSG-based syntactic treebank of Bulgarian (133–40). Apart from language-specific interests, the papers by Abeillé and colleagues and Hajičová and Sgall are particularly impressive. The former shows how a corpus tagged with higher accuracy than previous corpora can completely overturn research results based on corpora tagged with lower accuracy. The latter presents a corpus that is marked up for syntactic features, including topic-focus structure, with a depth that is probably unmatched in any language. The rest of the papers present corpus-based linguistic research. The paper by Beom-mo Kang, Hung-gyu Kim, and Myung-hoe Huh applies Douglas Biber’s multidimensional text analysis to Korean (51–57); Maarten Lemmens investigates the functions and meaning range of posture verbs in Swedish from a typological perspective (73–85); Martina Möllering analyzes the use of the German modal particle eben in a corpus of telephone conversations (87–97); P.-O. Nilsson shows how Swedish texts translated from English exhibit systematically different [End Page 904] lexical and grammatical patterns from those found in texts written originally in Swedish (99–107); Katja Ploog explores the syntax of pronominal subjects in Abidjanee French in contrast to standard French (123–32); and Adriana Vlad, Adrian Mitrea, and Mihai Mitrea investigate Romanian texts from a stochastic perspective (153–65). Many of these contributions combine quantitative corpus methods with qualitative methods of investigation and persuasively demonstrate how corpus-based research can contribute to broader linguistic issues. On the whole, not all papers in this volume are of the same theoretical interest, but each of them is at least informative. In contrast, the editing is rather disappointing. There is a table of contents and a...
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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) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.017 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.016 | 0.024 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.129 | 0.094 |
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".