2017 EMNLP EMNLP 2017

Labeling Gaps Between Words: Recognizing Overlapping Mentions with Mention Separators

Abstract

AbstractIn this paper, we propose a new model that is capable of recognizing overlapping mentions. We introduce a novel notion of mention separators that can be effectively used to capture how mentions overlap with one another. On top of a novel multigraph representation that we introduce, we show that efficient and exact inference can still be performed. We present some theoretical analysis on the differences between our model and a recently proposed model for recognizing overlapping mentions, and discuss the possible implications of the differences. Through extensive empirical analysis on standard datasets, we demonstrate the effectiveness of our approach.

🌉 Interdisciplinary Bridge — Artificial Intelligence and Machine Learning and Natural Language Processing
🧭 Keyword Pioneer — mention separator
🐣 Hot Topic Early Bird — graph representation
🐝 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