Bibliographic record
Abstract
This paper considers the problem of finding topical shifts in documents and in particular at what information can be leveraged to identify them. Recent research on topical segmentation usually assumes that topical shifts in discourse are signalled by changes in vocabulary. This information, however, is not always a sufficient indicator of a topical shift, especially for certain genres. This paper explores an additional source of information. Our hypothesis is that the type of a referring expression is an indicator of how accessible its antecedent is. The shorter and less informative the expression (e.g., a personal pronoun versus a lengthy post-modified noun phrase), the more accessible the antecedent is likely to be and the more likely it is that the topic under discussion has remained constant between the two mentions. We explore how this information can be used to augment a lexically-based topical segmenter. We test our hypothesis on two types of data, literary narratives and lecture notes. The results suggest that our similarity metric is useful: depending on the settings it either slightly improves the performance or leaves it unchanged. They also suggest that certain types of referring expressions are more useful than others. 1
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.
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".