Easy-to-Hard Learning for Information Extraction

Understand

Information extraction (IE) systems aim to automatically extract structured information, such as named entities, relations between entities, and events, from unstructured texts.

• While most existing work addresses a particular IE task, universally modeling various IE tasks with one model has achieved great success recently.
• Despite their success, they employ a one-stage learning strategy, i.e., directly learning to extract the target structure given the input text, which contradicts the human learning process.
• In this paper, we propose a unified easy-to-hard learning framework consisting of three stages, i.e., the easy stage, the hard stage, and the main stage, for IE by mimicking the human learning process.