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
Traditional pseudo relevance feedback (PRF) models choose top k feedback documents for query expansion and treat those documents equally. When k is determined, feedback terms are selected without considering the reliability of these documents for relevance. Because the performance of PRF is sensitive to the selection of feedback terms, noisy terms imported from these irrelevant documents or partially relevant documents will harm the final results extensively. Intuitively, terms in these documents should be considered less important for feedback term selection. Nonetheless, how to measure the reliability of feedback documents is a difficult problem. Recently, topic modeling has become more and more popular in the information retrieval (IR) area. In order to identify how reliable a feedback document is to be relevant, we attempt to adapt the topical information into PRF. However, topics are hard to be quantified and therefore the identification of topic is usually fuzzy. It is very challenging for integrating the obtained topical information effectively into IR and other text-processing-related areas. Current research work mainly focuses on mining relevant information from particular topics. This is extremely difficult when the boundaries of different topics are hard to define. In this article, we investigate a key factor of this problem, the topic number for topic modeling and how it makes topics “fuzzy.” To effectively and efficiently apply topical information, we propose a new probabilistic framework, “TopPRF,” and three models, TS-COS, TS-EU, and TS-Entropy, via integrating “Topic Space” (TS) information into pseudo relevance feedback. These methods discover how reliable a document is to be relevant through both term and topical information. When selecting feedback terms, candidate terms in more reliable feedback documents should obtain extra weights. Experimental results on various public collections justify that our proposed methods can significantly reduce the influence of “fuzzy topics” and obtain stable, good results over the strong baseline models. Our proposed probabilistic framework, TopPRF, and three topic-space-based models are capable of searching documents beyond traditional term matching only and provide a promising avenue for constructing better topic-space-based IR systems. Moreover, in-depth discussions and conclusions are made to help other researchers apply topical information effectively.
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 | 0.006 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.004 |
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