Latent semantic indexing and large dataset: Study of term-weighting schemes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The primary purpose of an information retrieval (IR) system is to retrieve all the relevant documents, which are relevant to the user query. Latent Semantic Indexing/Analysis (LSI/LSA) based ad hoc document retrieval task investigates the performance of retrieval systems that search a static set of documents using new questions. Performance of LSI has been tested by others for several smaller datasets (e.g. MED, CISI abstracts) however, LSI has not been tested for a large dataset. So, we decided to test LSI for a very large dataset. We used TREC-8 LA Times dataset for our experimentation. We applied three different term weighting schemes and our own stop word list to judge the performance. Recall-precision graph and Coefficient of Variation (CV) were used to evaluate the retrieval performance of LSI based retrieval system. We found tf-idf term weighting scheme performs better than log-entropy and raw term frequency weighting schemes when the test collection became very large.
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.
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 | 0.001 |
| 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 it