Improving Neural Sequence Labelling Using Additional Linguistic Information
Bibliographic record
Abstract
Sequence Labelling is the task of mapping sequential data from one domain to another domain. As we can interpret language as a sequence of words, sequence labelling is very common in the field of Natural Language Processing (NLP). In NLP, some fundamental sequence labelling tasks are Parts-of-Speech Tagging, Named Entity Recognition, Chunking, etc. Moreover, many NLP tasks can be modeled as sequence labelling or sequence to sequence labelling such as machine translation, information retrieval and question answering. An extensive amount of research has already been performed on sequence labelling. Most of the current high performing models are neural network models. These Deep Learning based models are outperforming traditional machine learning techniques by using abstract high dimensional feature representations of the input data. In this thesis, we propose a new neural sequence model which uses several additional types of linguistic information to improve the model performance. The convergence rate of the proposed model is significantly less than similar models. Moreover, our model obtains state of the art results on the benchmark datasets of POS, NER, and chunking.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".