Embedding and Language Modeling for Effective Text Mining - Jiawei Han
Jiawei Han is Michael Aiken Chair Professor in the Department of Computer Science, University of Illinois at Urbana-Champaign. He received ACM SIGKDD Innovation Award (2004), IEEE Computer Society Technical Achievement Award (2005), IEEE Computer Society W. Wallace McDowell Award (2009), and Japan’s Funai Achievement Award (2018). He is Fellow of ACM and Fellow of IEEE and served as the Director of Information Network Academic Research Center (INARC) (2009-2016) supported by the Network Science-Collaborative Technology Alliance (NS-CTA) program of U.S. Army Research Lab and co-Director of KnowEnG, a Center of Excellence in Big Data Computing (2014-2019), funded by NIH Big Data to Knowledge (BD2K) Initiative.
__________
The real-world big data are largely dynamic, interconnected and unstructured text. It is highly desirable to transform such massive unstructured data into structured knowledge. Many researchers rely on labor-intensive labeling and curation to extract knowledge from such data. Such approaches, however, are not scalable. We vision that massive text data itself may disclose a large body of hidden structures and knowledge. Equipped with pretrained language models and text embedding methods, it is promising to transform unstructured data into structured knowledge. In this talk, we introduce a set of methods developed recently in our group for such an exploration, including joint spherical text embedding, discriminative topic mining, taxonomy construction, text classification, and joint sentiment analysis. We show that data-driven approach could be promising at transforming massive text data into structured knowledge.
Subscribe to our newsletter and stay in the know:
https://www.iarai.ac.at/event-type/seminars/
_________
IARAI | Institute of Advanced Research in Artificial Intelligence
www.iarai.org
Видео Embedding and Language Modeling for Effective Text Mining - Jiawei Han канала IARAI Research
__________
The real-world big data are largely dynamic, interconnected and unstructured text. It is highly desirable to transform such massive unstructured data into structured knowledge. Many researchers rely on labor-intensive labeling and curation to extract knowledge from such data. Such approaches, however, are not scalable. We vision that massive text data itself may disclose a large body of hidden structures and knowledge. Equipped with pretrained language models and text embedding methods, it is promising to transform unstructured data into structured knowledge. In this talk, we introduce a set of methods developed recently in our group for such an exploration, including joint spherical text embedding, discriminative topic mining, taxonomy construction, text classification, and joint sentiment analysis. We show that data-driven approach could be promising at transforming massive text data into structured knowledge.
Subscribe to our newsletter and stay in the know:
https://www.iarai.ac.at/event-type/seminars/
_________
IARAI | Institute of Advanced Research in Artificial Intelligence
www.iarai.org
Видео Embedding and Language Modeling for Effective Text Mining - Jiawei Han канала IARAI Research
Показать
Комментарии отсутствуют
Информация о видео
Другие видео канала
Evaluating Machine (Human) Accuracy and Robustness on ImageNet - Ludwig SchmidtA Number Sense as an Emergent Property of the Manipulating Brain - Pietro PeronaModern Hopfield Networks - Dr Sepp HochreiterLearned data augmentation in natural language processing - Kyunghyun ChoCDCEO 22: Session I - Invited talk by Vipin KumarCDCEO 22: Session III - Invited talk by Nebojsa JojicTowards General and Robust AI at Scale - Irina RishPerformers & Memory - fireside chat: Sepp Hochreiter, Krzysztof Choromanski & Johannes BrandstetterScience4cast Special Session - Special Prize: Francisco AndradesScience4cast Special Session - 2nd Place: Ngoc TranProtein structure prediction with AlphaFold - Andrew SeniorTraffic4cast Special Session: Part II - NeurIPS 2020Neural diffusion PDEs, differential geometry, and graph neural networks - Michael BronsteinWeather4cast 2021 Special Session - Part 2Hopfield Networks in 2021 - Fireside chat between Sepp Hochreiter and Dmitry Krotov | NeurIPS 2020Machine Learning for Location Based Services - Prof. Dr. Ioannis GiannopoulosCDCEO22: Session II - Invited talk by Nantheera AnantrasirichaiScience4cast Special Session - 3rd Place: Milad AghajohariPatenting AI: Why, What and How? - Dr. Alexander KorenbergThe Importance of Motion Perception in Visual Recognition - Roman Pflugfelder