Application and evaluation of automated methods to extract neuroanatomical connectivity statements from free text
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
MOTIVATION: Automated annotation of neuroanatomical connectivity statements from the neuroscience literature would enable accessible and large-scale connectivity resources. Unfortunately, the connectivity findings are not formally encoded and occur as natural language text. This hinders aggregation, indexing, searching and integration of the reports. We annotated a set of 1377 abstracts for connectivity relations to facilitate automated extraction of connectivity relationships from neuroscience literature. We tested several baseline measures based on co-occurrence and lexical rules. We compare results from seven machine learning methods adapted from the protein interaction extraction domain that employ part-of-speech, dependency and syntax features. RESULTS: Co-occurrence based methods provided high recall with weak precision. The shallow linguistic kernel recalled 70.1% of the sentence-level connectivity statements at 50.3% precision. Owing to its speed and simplicity, we applied the shallow linguistic kernel to a large set of new abstracts. To evaluate the results, we compared 2688 extracted connections with the Brain Architecture Management System (an existing database of rat connectivity). The extracted connections were connected in the Brain Architecture Management System at a rate of 63.5%, compared with 51.1% for co-occurring brain region pairs. We found that precision increases with the recency and frequency of the extracted relationships. AVAILABILITY AND IMPLEMENTATION: The source code, evaluations, documentation and other supplementary materials are available at http://www.chibi.ubc.ca/WhiteText. CONTACT: paul@chibi.ubc.ca. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics Online.
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 | 0.001 |
| 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