Multi-source Machine Learning for Natural Language Relation Extraction

Vision

We develop multi-source information (relation and attribute) extraction techniques that are robust to limited labeled data and noise. We leverage semantic information and prompts for zero-shot relation extraction, and visual semantic information for multimodal multi-feature few-shot relation extraction. We also develop n-ary cross-sentence relation extraction methods for both supervised and unsupervised settings.

Funded Projects

• Natural Language Processing for Teaching and Research in Engineering Education – National Science Foundation (NSF) 2022
• Content Analytics for Smart Search – Xerox Workplace Innovation Research 2018
• Insider Threat Detection for Document Content Security – Xerox Workplace Innovation Research Program 2017

Publications

• Knowledge-Enhanced Multi-Label Few-Shot Product Attribute-Value Extraction – ACM CIMK 2023
• Clustering-based Unsupervised Generative Relation Extraction – IEEE Big Data 2022
• Unsupervised Relation Extraction: A Variational Autoencoder Approach – EMNLP 2021
• Zero-shot Relation Classification from Side Information – CIKM 2021 – code, data, presentation
• Prompt-based Zero-shot Relation Classification with Semantic Knowledge Augmentation Preprint 2021
• Reinforcement Learning-based N-ary Cross-Sentence Relation Extraction – Preprint 2020