Abstract Sentence Classification for Scientific Papers Based on Transductive SVM
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Presently, sentence-level researches are very significant in fields like natural language processing, information retrieval, machine translation etc. In this paper we present a practical task on sentence classification. The main purpose of this work is to classify the abstract sentences of scientific papers in the corpus built by ourselves into four categories- the background, the goal, the method and the result- which differ from each other in common usage, so that we can do further researches such as frequent pattern mining, information extraction and making a corpus for writing assistant system of scientific paper with these results. The main method of the classification is the Support Vector Machine, which is acknowledged among the best machine learning methods in the common text classification tasks. A semi-supervised method, Transductive Support Vector Machine, is also introduced into this four-class classification task to improve the accuracy. The experiments are conducted upon the corpus made by ourselves that consists of abstract sentences of scientific papers. The accuracy of the classifier finally reaches 75.86% with the semi-supervised method.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.011 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it