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This is an announcement of Zhi Hong's Dissertation Defense.<br class="">
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Candidate: Zhi Hong<br class="">
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Date: Friday, January 26, 2024<br class="">
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Time: 11 am CST<br class="">
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Location: JCL 390<br class="">
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Title: Enabling Scientific Information Extraction with Natural Language Processing<br class="">
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Abstract: Scientific discoveries have traditionally been communicated through written papers, but these documents pose challenges for computer understanding due to the inherent ambiguity and variability of natural language. Consequently, valuable knowledge
and groundbreaking insights often remain buried within an overwhelming volume of publications, rendering them undiscoverable, inaccessible, and unusable for both humans and machines. While efforts have been made to construct scientific databases and repositories
from these papers, these initiatives typically rely on laborious and error-prone manual extraction processes, which are not scalable to keep up with the millions of papers published annually. The inability to efficiently extract experimental data from existing
literature poses a significant obstacle that hinders the adoption of cost-effective, safe, and data-driven simulations to inform traditional experiments across multiple disciplines.<br class="">
To address this challenge and enable disciplines to leverage data-based simulations for cheaper, safer, and easier insights and guidance, Natural Language Processing (NLP) methods have emerged as a promising solution. Leveraging advanced machine learning techniques,
NLP empowers computers to analyze, comprehend, and derive meaningful information from the ever-expanding scientific literature.<br class="">
This work aims to explore various methodologies for automatically extracting precise and structured information from unstructured scientific text, ranging from traditional classifiers to deep neural networks and recent advancements in large language models.
By investigating these techniques, we seek to demonstrate their potential in facilitating real-world scientific research and enabling researchers to efficiently leverage the vast knowledge accumulated in scientific literature for accelerated scientific breakthroughs.<br class="">
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Advisors: Ian Foster and Kyle Chard<br class="">
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Committee Members: Ian Foster, Kyle Chard, and Raul Castro Fernandez<br class="">
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