Construction of Context Models for Word Sense Disambiguation
Bibliographic record
Abstract
This paper presents a study on the use of word context features for Word Sense Disambiguation (WSD). State-of-the-art WSD systems achieve high accuracy by using resources such as dictionaries, taggers, lexical analyzers or topic modeling packages. However, these resources are either too heavy or don’t have sufficient coverage for large-scale tasks such as information retrieval. The use of local context for WSD is common, but the rationale behind the formulation of features is often based on trial and error. We therefore investigate the notion of relatedness of context words to the target word (the word to be disambiguated), and propose an unsupervised method for finding the optimal weights for context words based on their distance to the target word. The key idea behind the method is that the optimal weights should maximize the similarity of two context models constructed from different context samples of the same word. Our experimental results show that the strength of the relation between two words follows approximately a power law. The resulting context models are used in Naïve Bayes classifiers for word sense disambiguation. Our evaluation on Semeval WSD tasks in both English and Japanese show that our method can achieve state-of-the-art effectiveness even though it does not use external tools like most existing methods. The high efficiency makes it possible to use our method in large scale applications such as information retrieval.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".