Semantic Understanding of General Linguistic Items by Means of Fuzzy Set Theory
Bibliographic record
Abstract
Modern statistical techniques used in the field of natural language processing are limited in their applications by the fact they suffer from the loss of most of the semantic information contained in text documents. Fuzzy techniques have been proposed as a way to correct this problem through the modelling of the relationships between words while accommodating the ambiguities of natural languages. However, these techniques are currently either restricted to modelling the effects of simple words or are specialized in a single domain. In this paper, we propose a novel statistical-fuzzy methodology to represent the actions described in a variety of text documents by modelling the relationships between subject-verb-object triplets. The research will focus in the first place on the technique used to accurately extract the triplets from the text, on the necessary equations to compute the statistics of the subject-verb and verb-object pairs, and on the formulas needed to interpolate the fuzzy membership functions from these statistics and on those needed to de fuzzify the membership value of unseen triplets. Taken together, these sets of equations constitute a comprehensive system that allows the quantification and evaluation of the meaning of text documents, while being general enough to be applied to any domain. In the second phase, this paper will proceed to experimentally demonstrate the validity of our new methodology by applying it to the implementation of a fuzzy classifier conceived especially for this research. This classifier is trained using a section of the Brown Corpus, and its efficiency is tested with a corpus of 20 unseen documents drawn from three different domains. The positive results obtained from these experimental tests confirm the soundness of our new approach and show that it is a promising avenue of research.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".