CUNY-BLENDER TAC-KBP2010 Entity Linking and Slot Filling System Description
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The CUNY-BLENDER team participated in the following tasks in TAC-KBP2010: Regular Entity Linking, Regular Slot Filling and Surprise Slot Filling task (per:disease slot). In the TAC-KBP program, the entity linking task is considered as independent from or a pre-processing step of the slot filling task. Previous efforts on this task mainly focus on utilizing the entity surface information and the sentence/document-level contextual information of the entity. Very little work has attempted using the slot filling results as feedback features to enhance entity linking. In the KBP2010 evaluation, the CUNY-BLENDER entity linking system explored the slot filling attributes that may potentially help disambiguate entity mentions. Evaluation results show that this feedback approach can achieve 9.1% absolute improvement on micro-average accuracy over the baseline using vector space model.
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.
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 | 0.001 | 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 it