Biomedical Word Sense Disambiguation with Contextualized Representation Learning
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
Representation learning is an important component in solving most Natural Language Processing (NLP) problems, including Word Sense Disambiguation (WSD). The WSD task tries to find the best meaning in a knowledge base for a word with multiple meanings (ambiguous word). WSD methods choose this best meaning based on the context, i.e., the words around the ambiguous word in the input text document. Thus, word representations may improve the effectiveness of the disambiguation models if they carry useful information from the context and the knowledge base. Most of the current representation learning approaches are that they are mostly trained on the general English text and are not domain specified. In this paper, we present a novel contextual-knowledge base aware sense representation method in the biomedical domain. The novelty in our representation is the integration of the knowledge base and the context. This representation lies in a space comparable to that of contextualized word vectors, thus allowing a word occurrence to be easily linked to its meaning by applying a simple nearest neighbor approach. Comparing our approach with state-of-the-art methods shows the effectiveness of our method in terms of text coherence.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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