Scientific Text Mining and Knowledge Graphs - Part 2-1
Explore scientific text mining and knowledge graphs in this comprehensive conference talk from KDD 2020. Delve into phrase mining techniques, including quality estimation and phrasal segmentation, with a focus on the SegPhrase and AutoPhrase algorithms. Learn about named entity recognition (NER), covering supervised methods, neural language models, and BERT. Discover distantly supervised NER approaches like SwellShark and AutoNER, with comparisons in the biomedical domain. Examine meta-pattern mining for information extraction, including pattern grouping and type adjustment. Investigate the PENNER framework for pattern-enhanced nested NER in biomedical literature, featuring weakly-supervised pattern expansion. Gain insights into cutting-edge techniques for mining and analyzing scientific text, presented by experts from the University of Notre Dame and the University of California, San Diego.