A Corpus-Based Analysis of the Adjectives and Synonyms -Beautiful, Handsome, and Pretty
Bibliographic record
Abstract
The aim of this study was to investigate the three synonyms beautiful, handsome and pretty in terms of their meanings and collocation with the help of the Longman Dictionary of contemporary English 6th edition (2014) and the British National Corpus (BNC). Only 100 concordance lines of each synonyms were selected from the corpora. This study was also aimed at investigating the similarities and differences between the three synonyms. The findings of this research declare that these synonyms are similar in their core meaning but are different in their detailed meanings and collocation. The results also clarifies that corpus provide more additional information that is not the part of dictionaries. It is also clear from the study that synonyms cannot be used in all the contexts alternately. Moreover, this study states that corpus is more helpful for the teachers of English as well as for second language learners (L2) because it gives additional information regarding any set of synonyms than dictionaries give. The teachers as well as students should be guided that they may get additional information about data from corpus than the dictionaries. As a result the students will be able in differentiating synonyms in a set by using both the resources, Learners Dictionaries and corpora, and in this way they will be able to increase their vocabulary.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".