Semi-Supervised Lexicon-Aware Embedding for News Article Time Estimation
Bibliographic record
Abstract
In the information retrieval community, Temporal Information Retrieval (TIR) has become increasingly popular. Documents focused on the time surrounding their publication are more likely to be accurate and contain information relevant to the reader. In this study, we explore the inverted pyramid paradigm by extracting temporal expressions from news documents, standardizing their values, and evaluating them based on their position within the text. We present a lexicon expansion method that employs WordNet as input. This approach enhances the lexicon by grouping words with similar meanings, potentially improving the accuracy of event detection algorithms. Additionally, this process can introduce new words and phrases to the lexicon, expanding the vocabulary. Using each tagged dataset, a classifier is trained with a pre-trained network. A pool of unlabeled data are processed, and high-confidence pseudo-labels are assigned. Pseudo-labels are generated by leveraging the partially trained model and the original labelled data. As the classifier predicts the correct label for a data sample, the pseudo-labels of other data samples are updated, and vice versa. At the end of this process, the predictions from different matching classifiers are combined. It takes several rounds to label the unlabeled inputs using this method. To evaluate the proposed solutions, we conducted experiments on 4,500 online news articles relevant to temporal retrieval. LSTM, BiLSTM, and BERT models with and without lexicon expansion were assessed based on log loss and relative divergence of entropy. A jointly trained semi-supervised learning model achieved a mean KL divergence of 0.89, an F1 score of 0.74 for temporal events, and 0.63 for non-temporal events. Besides alleviating data sparsity issues and enabling the training of more complex networks, this technique can also serve as an alternative to data augmentation methods.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".