Inner-character and Inner-word Features Based Representation Learning for Chinese Word Embedding
Why this work is in the frame
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Bibliographic record
Abstract
Chinese word embedding is a significant task in natural language processing (NLP). Most researchers explored Chinese word embedding according to radical, component, stroke n -gram and character features. Besides these features, Chinese characters still have structure and pinyin characteristics. In this article, we propose ensemble ssp2vec and connective ssp2vec to utilize inner-character features (stroke, structure, and pinyin) for learning Chinese word embeddings. Then we design hierarchical ssp2vec to forecast the contexts according to the combination of inner-character (stroke, structure, and pinyin) and inner-word features (character) of Chinese words to explore different feature combination ways for learning feature relevance and comprehending word semantics, where feature substring is proposed to learn the relevancy of stroke, structure, and pinyin. Experimental results for word analogy, word similarity, text classification, and named entity recognition tasks demonstrate that the proposed methods outperform most state-of-the-art models.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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