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2020 EMNLP EMNLP 2020

NwQM: A neural quality assessment framework for Wikipedia

Abstract

AbstractMillions of people irrespective of socioeconomic and demographic backgrounds, depend on Wikipedia articles everyday for keeping themselves informed regarding popular as well as obscure topics. Articles have been categorized by editors into several quality classes, which indicate their reliability as encyclopedic content. This manual designation is an onerous task because it necessitates profound knowledge about encyclopedic language, as well navigating circuitous set of wiki guidelines. In this paper we propose Neural wikipedia Quality Monitor (NwQM), a novel deep learning model which accumulates signals from several key information sources such as article text, meta data and images to obtain improved Wikipedia article representation. We present comparison of our approach against a plethora of available solutions and show 8% improvement over state-of-the-art approaches with detailed ablation studies.

🌉 Interdisciplinary Bridge — Computer Vision and Deep Learning and Machine Learning and Natural Language Processing
🧭 Keyword Pioneer — quality classification
🐣 Hot Topic Early Bird — document analysis
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

Bhanu Prakash Reddy Guda , Sasi Bhushan Seelaboyina , Soumya Sarkar , Animesh Mukherjee

Topics

Machine Learning > Core Methods > Classification Machine Learning > Application Areas > Data Augmentation Deep Learning > Architectures > Transformers Deep Learning > Architectures > Neural Networks Natural Language Processing > Applications > Text Classification Computer Vision > Domain-Specific > Document Analysis Deep Learning > Learning Types > Multi-Modal Learning

Keywords

feature extraction text classification multimodal learning text representation multi-modal learning deep learning document analysis deep learning model quality assessment neural network quality classification article representation article quality
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