2017 EACL EACL 2017

BabelDomains: Large-Scale Domain Labeling of Lexical Resources

Abstract

AbstractIn this paper we present BabelDomains, a unified resource which provides lexical items with information about domains of knowledge. We propose an automatic method that uses knowledge from various lexical resources, exploiting both distributional and graph-based clues, to accurately propagate domain information. We evaluate our methodology intrinsically on two lexical resources (WordNet and BabelNet), achieving a precision over 80% in both cases. Finally, we show the potential of BabelDomains in a supervised learning setting, clustering training data by domain for hypernym discovery.

🌉 Interdisciplinary Bridge — Interdisciplinary and Knowledge & Reasoning and Machine Learning and Natural Language Processing
📈 Trend Setter — Transfer Learning
🧭 Keyword Pioneer — domain labeling
🐝 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