Miller_and_Charles_with_concepts_senses
Bibliographic record
Abstract
This dataset represents the results of the experimentation of a method for evaluating semantic similarity between concepts in a taxonomy. The method is based on the information-theoretic approach and allows senses of concepts in a given context to be considered. The dataset is composed of 28 files. Each file refers to one pair of the well-known Miller and Charles benchmark dataset [1] for assessing semantic similarity. For each pair of concepts, the same 28 pairs are all considered as possible different contexts. We applied our proposal by extending 7 methods for computing semantic similarity in a taxonomy, selected from the literature. The methods considered in the experiment are referred to as (R[2], W&P[3], L[4], J&C[5], P&S[6], A[7], A&M[8]): REFERENCES [1] Miller, G.A., Charles, W.G. Contextual correlates of semantic similarity. Language and Cognitive Processes 6(1), 1-28 (1991) [2] Resnik, P. {\em Using Information Content to Evaluate Semantic Similarity in a Taxonomy}. In Proc. of the Int. Joint Conf. on Artificial Intelligence, Montreal, Quebec, Canada, August 20-25, Morgan Kaufmann, 448-453 (1995)]. [3] Wu, Z., Palmer, M. Verb semantics and lexical selection. In Proc. of the 32nd Annual Meeting of the Associations for Computational Linguistics, Las Cruces, New Mexico, 133-138 (1994). [4] Lin, D. An Information-Theoretic Definition of Similarity. In Proceedings of the Int. Conf. on Machine Learning, Madison, Wisconsin, USA. Morgan Kaufmann, 296-304 (1998). [5] Jiang, J.J., Conrath, D.W. Semantic Similarity Based on Corpus Statistics and Lexical Taxonomy. In Proc. of Inter. Conf. Research on Computational Linguistics (ROCLING X), Taiwan (1997). [6] Pirrò, G. A Semantic Similarity Metric Combining Features and Intrinsic Information Content. Data Knowl. Eng, 68(11), 1289-1308 (2009). [7] Adhikari, A., Dutta, B., Dutta, A., Mondal, D., Singh, S. An intrinsic information content-based semantic similarity measure considering the disjoint common subsumers of concepts of an ontology. J. Assoc. Inf. Sci. Technol. 69(8), 1023-1034 (2018). [8] Adhikari, A., Singh, S., Mondal, D., Dutta, B., Dutta, A. A Novel Information Theoretic Framework for Finding Semantic Similarity in WordNet. CoRR, arXiv:1607.05422, abs/1607.05422 (2016). Finally, in each file, the correlation of our proposal with respect to human judgement is reported.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.120 | 0.002 |
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; both teacher heads agree on what is shown here.
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".