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8 results for “Abuse detection”
Recalibrating classifiers for interpretable abusive content detection
<p>Dataset and code for the paper, 'Recalibrating classifiers for interpretable abusive content detection' by Vidgen et al. (2020) -- to appear at the <a href="https://sites.google.com/site/nlpandcss/">NLP + CSS workshop</a> at <a href="https://2020.emnlp.org">EMNLP 2020</a>.</p> <p>We provide:</p> <ol> <li>1,000 annotated tweets, sampled using the Davidson classifier with 20 0.05 increments (50 from each) from a dataset of tweets directed against MPs in the UK 2017 General Election</li> <li>1,000 annotated tweets, sampled using the Perspective classifier with 20 0.05 increments (50 from each) from a dataset of tweets directed against MPs in the UK 2017 General Election</li> <li>Code for recalibration in R and STAN.</li> <li>Annotation guidelines for both datasets.</li> </ol> <p> </p> <p><strong>Paper abstract</strong></p> <p>We investigate the use of machine learning classifiers for detecting online abuse in empirical research. We show that uncalibrated classifiers (i.e. where the 'raw' scores are used) align poorly with human evaluations. This limits their use to understand the dynamics, patterns and prevalence of online abuse. We examine two widely used classifiers (created by Perspective and Davidson et al.) on a dataset of tweets directed against candidates in the UK's 2017 general election.<br> A Bayesian approach is presented to recalibrate the raw scores from the classifiers, using probabilistic programming and newly annotated data. We argue that interpretability evaluation and recalibration is integral to the application of abusive content classifiers.</p> <p> </p> <p> </p>
Identification of New Serum Markers for Detection of Abuse With Erythropoietin
ClinicalTrials.gov study NCT01320449. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Benefit of Carbohydrate Deficient Transferrin to Detect Chronic Alcohol Abuse in the Elderly
ClinicalTrials.gov study NCT02822911. IPD Sharing: NO. Countries: 1. Publications: 0.
Data from: A support system for the detection of abusive clauses in B2C contracts
<p>Many countries employ systemic methods of protecting consumers from unfair business practices. One such practice is the use of abusive clauses in business-to-consumer (B2C) contracts, which unfairly impose additional obligations on the consumer or deprive them of their due rights. This article presents an information system that utilizes artificial intelligence methods to automate contract analysis and to detect abusive clauses. The goal of the system is to support the entire administrative process, from contract acquisition, through text extraction and the recommendation of potentially abusive clauses, to the generation of official administrative documents that can be sent to court or to the owners of firms. This article focuses on on the components that use machine learning methods. The first is an intelligent crawler that is responsible for automatically detecting contract templates on websites and retrieving them into the system. The second is a document analysis module that implements a clause recommendation algorithm. The algorithm employs transformer-based language models and information retrieval methods to identify abusive passages in text. Our solution achieved first place in a competition on the automatic analysis of B2C contracts organized by the Polish Office of Competition and Consumer Protection (UOKiK), and has since been implemented as an official tool to support the contract analysis process in Poland.</p>
Detecting Drugs of Abuse Via Exhaled Breath Samples Using Differential Mobility Spectrometry
ClinicalTrials.gov study NCT05384470. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Study Aimed at Detecting Potential Abuse of Nitrous Oxide in Children During Dental Care
ClinicalTrials.gov study NCT02894463. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Disseminating Child Abuse Clinical Decision Support to Improve Detection, Evaluation and Reporting
ClinicalTrials.gov study NCT03840798. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Identification of transcriptional biomarkers by RNA-sequencing for improved detection of ß2-agonists abuse in goat skeletal muscle
GEO Series GSE97479. Capra hircus. 4 samples. Type: Expression profiling by high throughput sequencing.
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