Impact of In-domain Vector Representations on the Classification of Disease-related Tweets
Bibliographic record
Abstract
A number of methods have been proposed for the construction of vector representations for natural language processing (NLP) tasks. These methods have been applied to various domains and each has its own pros and cons. Despite their effectiveness, the proposed approaches usually ignore the sentiment information concerning specific tasks. In this paper, we examined various types of word vectors and their impact on the performance of a sentiment classification problem in the area of infectious diseases. Vectors were used in the embedding layer of a word-based convolutional neural network (CNN) to identify tweets pertaining to avian influenza. We proposed a new approach to build effective word embeddings for the sentiment analysis task. Furthermore, the performance of the language model was compared in terms of using various corpus sizes and vector dimensions. Our experiments indicated that initializing the sentiment learning network with domain-specific word embeddings outperforms general domain embeddings. We found that the proposed method leads to a considerable improvement in the classification performance.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".