Classification of Micro-Texts Using Sub-Word Embeddings
Bibliographic record
Abstract
Extracting features and writing styles from short text messages is always a challenge.Short messages, like tweets, do not have enough data to perform statistical authorship attribution.Besides, the vocabulary used in these texts is sometimes improvised or misspelled.Therefore, in this paper, we propose combining four feature extraction techniques namely character n-grams, word n-grams, Flexible Patterns and a new sub-word embedding using the skip-gram model.Our system uses a Multi-Layer Perceptron to utilize these features from tweets to analyze short text messages.This proposed system achieves 85% accuracy, which is a considerable improvement over previous systems. Related WorksIn short-text analysis, one of the earlier works by Layton et al. (2010) aims to identify the author based on the data collected from micro-blogging websites like Twitter.The authors create author profiles using character level n-grams.They find the frequency of the most common n-grams in an author profile and assume that text from the same author would have a similar pattern.This approach is further extended by Schwartz et al. (2013); they use two more features namely word n-grams and a Hyponyms acquisition technique (Hearst, 1992) called Flexible patterns along with character n-grams.They then input a combination of these features into a linear SVM and ten-fold cross validation is applied to evaluate the model.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".