Learning the ontological positions of natural language objects
Bibliographic record
Abstract
This thesis endeavors to solve a text classification (TC) problem of a real-world system, New Brunswick Opportunities Network (NBON), an online tendering system that helps the vendors and the purchasing agents to provide and obtain information about business opportunities. The solution mainly involves techniques in the areas of machine learning and natural language processing (NLP). We use a Naïve Bayes classifier, a simple and effective machine learning approach for TC tasks, to automatically classify the tenders of the NBON system. We implement three smoothing algorithms for the Naïve Bayes classifier, namely, no-match, Laplace correction, Lidstone's law of succession, and we show that the difference between the accuracies obtained for the three algorithms is negligible. We show that the effectiveness of the Naïve Bayes classifier is better than that of three other TC techniques that are equally simple, namely, Strong Predictors (a modification of Term Frequency), TF-IDF (Term Frequency - Inverse Document Frequency), and WIDF (Weighted Inverse Document Frequency). NLP tools such as stop lists and stemmers are adopted for the text operations on the historic NBON data that is used to train the classifiers. We experiment with variations of such tools and show that NLP techniques do not have much impact on the effectiveness of a classifier.
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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.013 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".