Vocab Learn: A Text Mining System to Assist Vocabulary Learning
Bibliographic record
Abstract
We present a text mining system called Vocab Learn to assist users to learn new words with respect to a knowledge base, where a knowledge base is a collection of written materials. Vocab Learn extracts words, excluding stop words, from a knowledge base and recommends new words to a user according to their importance and frequency. To enforce learning and assess how well a word is learned, Vocab Learn generates, for each word recommended, a number of semantically close words using word embeddings (Mikolov et al., 2013a), and a number of words with look-alike spellings/strokes but with different meanings using Minimum Edit Distance (Levenshtein, 1966). Moreover, to help learn how to use a new word, Vocab Learn links each word to its dictionary definitions and provides sample sentences extracted from the knowledge base that includes the word. We carry out experiments to compare word-ranking algorithms of TFIDF (Salton and McGill, 1986), TextRank (Mihalcea and Tarau, 2004), and RAKE (Rose et al., 2010) over the dataset of Inspec abstracts in Computer Science and Information Technology Journals with a set of keywords labeled by human editors. We show that TextRank would be the best choice for ranking words for this dataset. We also show that Vocab Learn generates reasonable words with similar meanings and words with similar spellings but with different meanings.
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 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 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.010 |
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".