Subword Latent Semantic Analysis for TextTiling-based Automatic Story Segmentation of Chinese Broadcast News
Bibliographic record
Abstract
This paper proposes to perform latent semantic analysis (LSA) on character/syllable n-gram sequences of automatic speech recognition (ASR) transcripts, namely subword LSA, as an extension of our previous work on subword TextTiling for automatic story segmentation of Chinese broadcast news. LSA represents the 'meaning' of a lexical term by a feature vector conveying the term's relations with other terms. We apply subword LSA vectors to the measurement of inter-sentence lexical score in TextTiling-based story segmentation. Subword n-grams are robust to speech recognition errors, especially out-of-vocabulary (OOV) words, in lexical matching on Chinese ASR transcripts. This work combines the concept matching merit of LSA and the robustness of subwords. Experimental results on the TDT2 Mandarin corpus show that subword-LSA-based TextTiling can effectively improve the story segmentation performance. Character-bigram-LSA-based TextTiling achieves the best F1-measure of 0.6598 with relative improvement of 17.4% over the conventional word-based TextTiling and 6.5% over our previous syllable-bigram-based TextTiling.
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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 | 0.000 | 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 | 0.000 | 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".