A latent concept topic model for robust topic inference using word embeddings
Bibliographic record
Abstract
Uncovering thematic structures of SNS and blog posts is a crucial yet challenging task, because of the severe data sparsity induced by the short length of texts and diverse use of vocabulary. This hinders effective topic inference of traditional LDA because it infers topics based on document-level co-occurrence of words. To robustly infer topics in such contexts, we propose a latent concept topic model (LCTM). Unlike LDA, LCTM reveals topics via co-occurrence of latent concepts, which we introduce as latent variables to capture conceptual similarity of words. More specifically, LCTM models each topic as a distribution over the latent concepts, where each latent concept is a localized Gaussian distribution over the word embedding space. Since the number of unique concepts in a corpus is often much smaller than the number of unique words, LCTM is less susceptible to the data sparsity. Experiments on the 20Newsgroups show the effectiveness of LCTM in dealing with short texts as well as the capability of the model in handling held-out documents with a high degree of OOV words
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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".