Crowd-Labeling Fashion Reviews with Quality Control
We present a new methodology for high-quality labeling in the fashion domain with crowd workers instead of experts. We focus on the Aspect-Based Sentiment Analysis task. Our methods filter out inaccurate input from crowd workers but we preserve different worker labeling to capture the inherent high variability of the opinions. We demonstrate the quality of labeled data based on Facebook's FastText framework as a baseline.
Code (1)
Tasks
Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)Sentiment AnalysisSimilar Papers 제목 키워드 기반
Classification and Geotemporal Analysis of Quality-of-Life Issues in Tenant Reviews
Online tenant reviews of multifamily residential properties present a unique source of information for commercial real estate investing and research. Real estate professionals frequently read tenant reviews to uncover pr…
text-classificationText ClassificationDistributional Ground Truth: Non-Redundant Crowdsourcing Data Quality Control in UI Labeling Tasks
HCI increasingly employs Machine Learning and Image Recognition, in particular for visual analysis of user interfaces (UIs). A popular way for obtaining human-labeled training data is Crowdsourcing, typically using the q…
LabelAId: Just-in-time AI Interventions for Improving Human Labeling Quality and Domain Knowledge in Crowdsourcing Systems
Crowdsourcing platforms have transformed distributed problem-solving, yet quality control remains a persistent challenge. Traditional quality control measures, such as prescreening workers and refining instructions, ofte…
Crowdsourcing Natural Language Data at Scale: A Hands-On Tutorial
In this tutorial, we present a portion of unique industry experience in efficient natural language data annotation via crowdsourcing shared by both leading researchers and engineers from Yandex. We will make an introduct…
CrowdAgent: Multi-Agent Managed Multi-Source Annotation System
High-quality annotated data is a cornerstone of modern Natural Language Processing (NLP). While recent methods begin to leverage diverse annotation sources-including Large Language Models (LLMs), Small Language Models (S…