Text Representation with WordNet Synsets Using Soft Sense Disambiguation
Bibliographic record
Abstract
Abstract: Text information processing depends critically on the proper representation of texts. A common and naive way of representing a text is as a bag of its component words. This representation suffers primarily from two drawbacks, viz., polysemy and synonymy which arise because of the ambiguity of the words and the lack of information about the relations between the words. This paper presents a model for representing a text in terms of the synsets in the WordNet- the lexical knowledge base of English words along with the semantic relations. These synsets stand for concepts which correspond to the words of the text. In particular, a soft sense disambiguation approach has been proposed. The text representation so obtained is found to convey the key ideas that the texts deal with. WordNet relations with other words in the sentence are exploited to disambiguate the senses. This scheme has been evaluated using a goodness measure based the information content of the representation of the text. As an actual application, the problem of text classification has been taken up, and the results are encouraging.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".