Text Representation and Similarity Measure for Text Clustering Based on Semantic Strings: A Case Study on Uyghur Language
Bibliographic record
Abstract
ABSTRACT In Uyghur language, the words which are segmented by inter-word space as natural separator can hardly serve as features in text representation, which leads to the low efficiency of text processing, it is still a research topic how to use language units beyond word boundaries as features to represent texts and improve the efficiency of text processing. This paper proposes a semantic string extraction approach, which is a method for extracting language units beyond word boundaries. At the same time, it also proposes the methods for textual representation and similarity measurement, and verifies its effectiveness in Uyghur text clustering tasks. Specifically, a combination of string expansion and language rules are applied to identify the trusted frequent patterns (TFP) in the text set. Next, semantic strings are evaluated and selected from the text set. Regarding similarity measure, each text is represented as a weighted semantic string set, and a set-based text similarity measuring approach is presented. Finally, the above ideas and approaches are applied to the Uyghur text clustering, and the corresponding clustering algorithms are proposed and verified through series of experiments on the large-scale text corpus. Experimental results show that the semantic string-based text representation is in general very useful in processing Uyghur language.
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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 | 0.001 | 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".