90% F1 Score in Relation Triple Extraction: Is it Real? [PDF]
Proceedings of the 1st GenBench Workshop on (Benchmarking) Generalisation in NLP, 2023 Extracting relational triples from text is a crucial task for constructing knowledge bases. Recent advancements in joint entity and relation extraction models have demonstrated remarkable F1 scores (≥ 90%) in accurately extracting relational triples from
Pratik Saini +3 more
semanticscholar +5 more sources
The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. [PDF]
BMC Genomics, 2020 To evaluate binary classifications and their confusion matrices, scientific researchers can employ several statistical rates, accordingly to the goal of the experiment they are investigating.
Chicco D, Jurman G.
europepmc +7 more sources
Keeping Pathologists in the Loop and an Adaptive F1-Score Threshold Method for Mitosis Detection in Canine Perivascular Wall Tumours. [PDF]
Cancers (Basel)Simple Summary Performing a mitosis count (MC) is essential in grading canine Soft Tissue Sarcoma (cSTS) and canine Perivascular Wall Tumours (cPWTs), although it is subject to inter- and intra-observer variability.
Rai T +8 more
europepmc +5 more sources
Biomedical and Biotechnology Research Journal, 2021 Background: Biomedical field has gained a lot of interest from active researchers today. Treating various diseases prevailing among the world has believed to bring huge insight in the today's research world.
Disha Harshadbhai Parekh, Vishal Dahiya
doaj +3 more sources
Applied SciencesThis study compares various F1-score variants—micro, macro, and weighted—to assess their performance in evaluating text-based emotion classification. Lexicon distillation is employed using the multilabel emotion-annotated datasets XED and GoEmotions. The
Maria Cristina Hinojosa Lee +2 more
doaj +4 more sources
Maximum F1-Score Training for End-to-End Mispronunciation Detection and Diagnosis of L2 English Speech [PDF]
2022 IEEE International Conference on Multimedia and Expo (ICME), 2021 End-to-end (E2E) neural models are increasingly attracting attention as a promising modeling approach for mispronunciation detection and diagnosis (MDD).
Bi-Cheng Yan +4 more
semanticscholar +4 more sources
Machine-learning classification of astronomical sources: estimating F1-score in the absence of ground truth [PDF]
Monthly Notices of the Royal Astronomical Society: Letters, 2022 Machine-learning based classifiers have become indispensable in the field of astrophysics, allowing separation of astronomical sources into various classes, with computational efficiency suitable for application to the enormous data volumes that wide ...
A. Humphrey +7 more
semanticscholar +3 more sources
Probabilistic Extension of Precision, Recall, and F1 Score for More Thorough Evaluation of Classification Models [PDF]
Proceedings of the First Workshop on Evaluation and Comparison of NLP Systems, 2020 In pursuit of the perfect supervised NLP classifier, razor thin margins and low-resource test sets can make modeling decisions difficult. Popular metrics such as Accuracy, Precision, and Recall are often insufficient as they fail to give a complete ...
Reda Yacouby, Dustin Axman
semanticscholar +2 more sources
An intruder from another world: F1-score.
Revista Electrónica AnestesiaREl F1-score, también llamado F-score o medida F, es un estimador de la capacidad de clasificación de una prueba que se usa con frecuencia en la ciencia de datos y en los algoritmos de inteligencia artificial y que puede ser de utilidad para la valoración
Manuel Molina
semanticscholar +4 more sources
Anomaly Detection: How to Artificially Increase your F1-Score with a Biased Evaluation Protocol [PDF]
ECML/PKDD, 2021 Anomaly detection is a widely explored domain in machine learning. Many models are proposed in the literature, and compared through different metrics measured on various datasets.
Damien Fourure +3 more
semanticscholar +3 more sources