Behavior Research Methods, Instruments, Computers
Bibliographic record
Abstract
this article, we describe our efforts in using state-of-the-art natural language analysis technolo113 Copyright 2004 Psychonomic Society, Inc. This work was supported by a grant from the National Science Foundation Linguistic Program to B. M.Thanks to ChristopheParisse for creating the POST program and to Yuriko Oshima-Takane and her students at McGill for producing these disambiguated tags for the training of POST. Maria Lamendola assisted with hand-checking of the results of LCFlex. Correspondenceconcerning this article should be addressed to K. Sagae, LanguageTechnologies Institute, Carnegie Mellon University, 5000Forbes Avenue, Pittsburgh,PA 15213 (e-mail: sagae@cs.cmu.edu). Automatic parsing of parental verbal input KENJI SAGAE, BRIAN MACWHINNEY, and ALON LAVIE Carnegie Mellon University, Pittsburgh, Pennsylvania To evaluate theoretical proposals regarding the course of child language acquisition, researchers often need to rely on the processing of largenumbers of syntacticallyparsed utterances,both from children and from their parents. Because it is so difficult to do this by hand, there are currently no parsed corpora of child language input data. To automate this process, we developed a system that combined the MOR tagger, a rule-based parser, and statistical disambiguation techniques. The resultant system obtained nearly 80% correct parses for the sentences spoken to children. To achieve this level, we had to construct a particular processing sequence that minimizes problems caused by the coverage/ ambiguity tradeoff in parser design. These procedures are particularly appropriate for use with the CHILDES database, an international corpus of transcripts. The data and programs are now freely available over the Internet
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.026 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.038 | 0.014 |
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".