Bibliographic record
Abstract
BOOK NOTICES 199 Several ofthe papers extend natural language parsing into new territory. For example, Mats Wirén and Ralph Ronnquist ('Fully incremental parsing', 1 1-34) present a technique for parsing a text as it is typed and responding immediately to changes made anywhere in it. This is important for online grammar checking, language translation, and the like. Incremental parsing of programming languages has been around for a long time, but incremental parsing of natural language is, as far as I know, a new idea. Rudi Lutz ('Flowgraph parsing', 359-83) and Kent Wittenburg ('Predictive parsing for unordered relational languages', 385-407) extend parsing techniques to two-dimensional diagrams and flowcharts . After all, a diagram, like a sentence, is made of recognizable elements connected in a limited set of ways; some relation between natural language parsing and computer vision has been evident for some time, and the parsing of diagrams is perhaps the point of greatest contact. At least four papers deal in some way with dependency grammar or syntactic headship. Makoto Nagao ('Varieties of heuristics in sentence parsing', 79-90) advocates dependency analysis as the best approach to machine translation of Japanese. He is not alone; to judge from the papers presented at COLING-98 (Montréal, 1998, four years after the conferences reported in this volume), interest in dependency parsing is on the rise. Klaas Sikkel and Rieks op den Akker ('Predictive head-corner parsing', 169-82) build syntactic tree structures from the head outward. Papers by Stephan Raaijmakers (115-44) and Harry Bunt and Ko van der Sloot (91-1 14) use HPSG (headdriven phrase-structure grammar); the latter also uses Bunt's own formalism, DPSG (discontinuous phrase-structure grammar), i.e. constituency grammar with crossing branches. What is missing, of course, is transformational grammar, which is generally considered unsuitable for parsing. Even Eric Brill's interesting paper, 'Learning to parse with transformations' (221-40), turns out to deal with transforming a phrase-structure parser to improve its output rather than using transformations in the Chomskyan sense. [Michael A. Covington, University of Georgia.] Towards a 'natural' narratology. By Monika Fludernik. London: Routledge , 1996. Pp. xvi, 454. Encyclopedic in its coverage of examples and richly synthetic in its combination of theoretical paradigms for studying narratives (oral as well as written ), this book marks a significant contribution to several research domains. Analyzing the structures and functions of narrative from both a (synchronic) cognitive and a historical perspective, Fludernik addresses issues that will be of interest to discourse analysts, cognitive scientists, historians of the English language and of its literature, narratologists, theorists and historians of prose, and students of the historical and conceptual interconnections between orality and literacy. Chronicling the emergence of prose narrative in English, F develops a historical argument about how the experimental techniques found in the elaborate narratives of contemporary literature can ultimately be traced back to techniques used in 'simpler' conversational storytelling. Whatever their location along the scale of narrative complexity, stories rely crucially on what F descnbes as four cognitive parameters in her first chapter, 'Towards a "natural" narratology' (12-52). These four parameters include the real-world script of telling; the real-world schema of perception or viewing; the parameter by which one gains access to experiences that can then be told about or narrativized, i.e. experiencing; and finally the parameter of action or acting. Ch. 2, 'Natural narrative and other oral modes' (53-91), shows how these parameters guide the use and interpretation of stories in conversational settings. Yet—and this is one of F's chief claims in the book—the same basic parameters allow story recipients to 'narrativize ' (interpret as narratively structured) instances of discourse that can be quite difficult to comprehend in narrative terms. In the author's words, when faced with, say, challenging literary narratives of recent years, readers 'attempt to re-cognize what they find in the text in terms of the natural telling or experiencing or viewing parameters, or they try to recuperate the inconsistences in terms of actions and event structures at the most minimal level' (34). Hence the transition from oral to written narrative should be conceived as a continuum, and researchers should...
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.009 |
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".