Title: Structured Minimally Supervised Learning for Neural Relation Extraction

Abstract: In this talk, I will describe an effort to extract structured knowledge from text, without relying on slow and expensive human labeling (accepted to NAACL 2019). Our approach combines the benefits of learned representations and structured learning, and accurately predicts sentence-level relation mentions given only proposition-level supervision from a knowledge base. By explicitly reasoning about missing data during learning, this method enables large-scale training of convolutional neural networks while mitigating the issue of label noise inherent in distant supervision. Our approach achieves state-of-the-art results on minimally supervised sentential relation extraction, outperforming a number of baselines, including a competitive approach that uses the attention layer of a purely neural model.

Bio: Fan Bai is a third-year PhD student in the Department of Computer Science and Engineering, advised by Prof. Alan Ritter. His research mainly focuses on extracting structured knowledge from large corpus under distant supervision.

Title: Diving for Pearls: Indexing Mobility Information in Clinical Records with a Neural Relevance Tagger

Abstract: Locating sparse information in medical text that is relevant to reported functional limitations is a significant challenge in the US Social Security Administration’s (SSA) process of determining disability. In this talk, I will introduce HARE, a system for highlighting relevant information in document collections for retrieval and triage (accepted to EMNLP 2019), and describe applications of this tool to retrieve narrative descriptions of mobility limitations in NIH and SSA records. I will demonstrate that tagging for relevance at the token level achieves high recall on retrieving true mobility descriptions, and ranking documents by the number of predicted mobility-relevant segments achieves strong correlation with ranking by true mobility information. Additionally, I will show that static word embedding features and contextualized ELMo and BERT features yield substantially different patterns in system outputs, and describe several patterns identified through qualitative analysis that suggest clear directions for further research on improving indexing of functional status information.

Bio: Denis is a 6th-year PhD student in the Department of Computer Science and Engineering, studying with Dr. Eric Fosler-Lussier. He is a Pre-Doctoral Fellow of the National Institutes of Health Clinical Center since 2015, and has led pioneering research on natural language processing methods for functional status information, particularly in the domain of mobility. His research areas include information extraction and retrieval, linguistic analysis of clinical data, and representation learning, and his work has been funded by the Intramural Program of the National Institutes of Health and the US Social Security Administration.