Deep Neural Networks for Part-of-Speech Tagging in Under-Resourced Amazigh
Bibliographic record
Abstract
Part-of-speech (POS) tagging denotes the assignment of appropriate grammatical categories to individual words within a sentence or text, playing a pivotal role in numerous natural language processing (NLP) tasks.While POS tagging in widely-used languages such as English has reached accuracy levels exceeding 97%, less-resourced languages such as Amazigh have seen limited research and therefore, less accuracy in tagging efforts.This paper aims to bridge this gap by exploring the application of deep learning models for Amazigh POS tagging, specifically focusing on various types of recurrent neural networks (RNNs)-gated recurrent networks, long short-term memory (LSTM) networks, and bidirectional LSTM networks.Despite the relatively small dataset of 60k tokens, a stark contrast to the vast corpuses available for languages with extensive resources, the proposed RNN models have demonstrated significant improvements over existing Amazigh POS taggers.Remarkably, all RNN models tested in this study outperformed traditional machine learning taggers, achieving an accuracy rate of 97%, thus presenting a promising avenue for enhanced POS tagging in under-resourced languages.This research underscores the potential of deep learning approaches in contributing to the advancement of linguistic studies in less-documented languages, such as Amazigh.
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 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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".