A Textual Entailment System using Anaphora Resolution.
Bibliographic record
Abstract
The note describes the Recognizing Textual Entailment (RTE) system developed at the Computer Science and Engineering Department, Jadavpur University, India. In this competition, we have participated and submitted the results in the RTE-7 Main Task (3 runs), Novelty Task (3 runs) and RTE-7 KBP Validation task (2 unique runs for generic task and 2 unique runs for tailored task). For the RTE7 Main and Novelty Tasks, the systems are based on pre-processing task which includes Anaphora Resolution using JavaRAP tool then the system is the composition of Lexical Entailment module, Syntactic Entailment module, Chunk module and Named Entity module. For the RTE-7 Main task test set, the following micro-average results were obtained for Run 1: F-Score 29.81, Run 2: F-Score 30.47 and Run 3: F-score 29.90. For the RTE-7 Novelty task test set, the following micro-average results were obtained for Run 1: Novelty Evaluation F-Score 86.26 and Justification Evaluation F-Score 20.02, Run 2: Novelty Evaluation F-Score 78.49 and Justification Evaluation F-Score 26.56 and Run 3: Novelty Evaluation F-score 73.94 and Justification Evaluation F-Score 25.55 were obtained. The RTE-7 KBP Validation Task is based on the assumption that extracted slot filler is correct if and only if the supporting document entails a hypothesis created on the basis of the slot filler. In RTE KBP, we participated for generic task and tailored task. For the RTE-7 KBP Validation task test set for Generic Task, micro-average results for Run 1: F-Score 0.148 and Run 2: F-Score 0.1902 were obtained. For RTE-7 KBP test set for Tailored Task, micro-average results for Run 1: F-Score 0.1813, Run 2: F-Score and 0.1834 were obtained.
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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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".