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 subject of one’s opinions expressed in textual data provides rich information regarding their attitudes and behaviors. Many natural language processing tasks leverage such information to, for example, study product purchasing behaviors or extract insights during global events. The task of identifying these subjects is referred to as aspect extraction . Aspect extraction approaches typically focus on the identification of explicitly stated aspects in a text sample. However, it is suggested that implicit aspects , or those that must be inferred by the context provided in the text, comprise more than 20% of all aspects in a given dataset and that identification of implicit aspects is important for accurate aspect-based analyses such as aspect-based sentiment analysis. As such, this article surveys recent work in implicit aspect extraction. We define and describe commonly used datasets and algorithmic approaches and detail various challenges that have thus far led to limited research in implicit aspect extraction as compared to explicit aspect extraction, like fewer benchmark datasets and limited use of powerful attention models.
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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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