Jian Ni

Cascaded Models for Better Fine-Grained Named Entity Recognition

2020 · arXiv (Cornell University)

Named Entity Recognition (NER) is an essential precursor task for many natural language applications, such as relation extraction or event extraction. Much of the NER research has been done on datasets with few classes of …

Distilling Event Sequence Knowledge From Large Language Models

2024 · arXiv (Cornell University)

Event sequence models have been found to be highly effective in the analysis and prediction of events. Building such models requires availability of abundant high-quality event sequence data. In certain applications, however, clean structured event …

Weakly Supervised Cross-Lingual Named Entity Recognition via Effective Annotation and Representation Projection

2017

The state-of-the-art named entity recognition (NER) systems are supervised machine learning models that require large amounts of manually annotated data to achieve high accuracy. However, annotating NER data by human is expensive and time-consuming, and …