Tootfinder

A Fisher's exact test justification of the TF-IDF term-weighting scheme
Paul Sheridan, Zeyad Ahmed, Aitazaz A. Farooque
https://arxiv.org/abs/2507.15742

A Fisher's exact test justification of the TF-IDF term-weighting scheme
Term frequency-inverse document frequency, or TF-IDF for short, is arguably the most celebrated mathematical expression in the history of information retrieval. Conceived as a simple heuristic quantifying the extent to which a given term's occurrences are concentrated in any one given document out of many, TF-IDF and its many variants are routinely used as term-weighting schemes in diverse text analysis applications. There is a growing body of scholarship dedicated to placing TF-IDF on a sound …

Ranking Methods for Skyline Queries
Micka\"el Martin-Nevot (AMU), Lotfi Lakhal (AMU)
https://arxiv.org/abs/2507.21860 https://arxiv.org/pdf/2507.21860…

Ranking Methods for Skyline Queries
{Multi-criteria decision analysis in databases has been actively studied, especially through the Skyline operator. Yet, few approaches offer a relevant comparison of Pareto optimal, or Skyline, points for high cardinality result sets. We propose to improve the dp-idp method, inspired by tf-idf, a recent approach computing a score for each Skyline point, by introducing the concept of dominance hierarchy. As dp-idp lacks efficiency and does not ensure a distinctive rank, we introduce the RankSky …

TriagerX: Dual Transformers for Bug Triaging Tasks with Content and Interaction Based Rankings
Md Afif Al Mamun, Gias Uddin, Lan Xia, Longyu Zhang
https://arxiv.org/abs/2508.16860

TriagerX: Dual Transformers for Bug Triaging Tasks with Content and Interaction Based Rankings
Pretrained Language Models or PLMs are transformer-based architectures that can be used in bug triaging tasks. PLMs can better capture token semantics than traditional Machine Learning (ML) models that rely on statistical features (e.g., TF-IDF, bag of words). However, PLMs may still attend to less relevant tokens in a bug report, which can impact their effectiveness. In addition, the model can be sub-optimal with its recommendations when the interaction history of developers around similar bug…

Searching the Title of Practical Work of the Informatics Engineering Bachelor Program with the Case Base Reasoning Method
Agung Sukrisna Jaya, Osvari Arsalan, Danny Matthew Saputra
https://arxiv.org/abs/2508.20442

Searching the Title of Practical Work of the Informatics Engineering Bachelor Program with the Case Base Reasoning Method
Case Base Reasoning (CBR) is a case solving technique based on experience in cases that have occurred before with the highest similarity. CBR is used to search for practical work titles. TF-IDF is applied to process the vectorization of each practical work title word and Cosine Similarity for the calculation of similarity values. This system can search either in the form of titles or keywords. The output of the system is the title of practical work and the match value of each title. Based on th…

Semantic Search for Information Retrieval
Kayla Farivar
https://arxiv.org/abs/2508.17694 https://arxiv.org/pdf/2508.17694

Semantic Search for Information Retrieval
Information retrieval systems have progressed notably from lexical techniques such as BM25 and TF-IDF to modern semantic retrievers. This survey provides a brief overview of the BM25 baseline, then discusses the architecture of modern state-of-the-art semantic retrievers. Advancing from BERT, we introduce dense bi-encoders (DPR), late-interaction models (ColBERT), and neural sparse retrieval (SPLADE). Finally, we examine MonoT5, a cross-encoder model. We conclude with common evaluation tactics,…