Bibliographic record
Abstract
The phenomenon of code mixing (CM), particularly between Hindi and English, is increasingly prevalent in digital communication, especially on social media. The linguistic features of CM content often contain informal grammar, spelling errors, transliteration, etc., which makes the task of Natural Language Processing (NLP) of such content hard. This paper discusses the implementation of a Part-of-Speech (POS) tagger built using a single-layer Perceptron for CM content on social media and analyses the sources of inaccuracies in tagging. The tagger achieves an accuracy of 84%. The study tries to understand the types of sentence patterns that lead to tagging errors. It shows the correlation of tagging errors with neighbouring word context, sentence length, etc. Certain misclassifications are found to be more common than others, for example, nouns for verbs, which may be related to the difference in the way the two languages place verbs in sentences. POS tagging errors have also been shown to have a cascading effect: one error in a sentence leads to more, and isolated errors are less common. Understanding these error patterns, including common misclassifications, shows how these errors are linked to linguistic features like sentence structure, and highlights areas of improvement for further research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".