Bibliographic record
Abstract
Semantic analysis is the process of shifting the understanding of text from the levels of phrases, clauses, sentences to the level of semantic meanings. Two of the most important semantic analysis tasks include 1) semantic relatedness measurement and 2) entity linking. The semantic relatedness measurement task aims to quantitatively identify the relationships between two words or concepts based on the similarity or closeness of their semantic meaning whereas the entity linking task focuses on linking plain text to structured knowledge resources, e.g. Wikipedia to provide semantic annotation of texts. A limitation of current semantic analysis approaches is that they are built upon traditional documents which are well structured in formal English, e.g. news; however, with the emergence of social networks, enormous volumes of information can be extracted from the posts on social networks, which are short, grammatically incorrect and can contain special characters or newly invented words, e.g. LOL, BRB. Therefore, traditional semantic analysis approaches may not perform well for analysing social network posts. In this thesis, we build semantic analysis techniques particularly for Twitter content. We build a semantic relatedness model to calculate semantic relatedness between any two words obtained from tweets and by using the proposed semantic relatedness model, we semantically annotate tweets by linking them to Wikipedia entries. We compare our work with state-of-the-art semantic relatedness and entity linking methods that show promising results.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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".