2020 EMNLP EMNLP 2020

Improved Local Citation Recommendation Based on Context Enhanced with Global Information

Abstract

AbstractLocal citation recommendation aims at finding articles relevant for given citation context. While most previous approaches represent context using solely text surrounding the citation, we propose enhancing context representation with global information. Specifically, we include citing article’s title and abstract into context representation. We evaluate our model on datasets with different citation context sizes and demonstrate improvements with globally-enhanced context representations when citation contexts are smaller.

🌉 Interdisciplinary Bridge — Data Science & Analytics and Machine Learning and Natural Language Processing
🧭 Keyword Pioneer — local citation
🐝 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