Lecture

Coreference Resolution

Description

This lecture covers coreference resolution, a natural language processing task that involves identifying when two or more expressions in text refer to the same entity. The instructor discusses the challenges of mention detection, different coreference resolution models, and the current state-of-the-art neural systems. The lecture also explores the applications of coreference resolution in full text understanding, machine translation, and dialogue systems. Various linguistic concepts related to coreference, anaphora, and cataphora are explained, along with traditional algorithms like Hobbs' naive algorithm. The lecture concludes with an overview of end-to-end neural coreference models and the latest advancements using BERT-based approaches.

This video is available exclusively on Mediaspace for a restricted audience. Please log in to MediaSpace to access it if you have the necessary permissions.

Watch on Mediaspace
About this result
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.