no code implementations • 30 Dec 2019 • Xiaotong Liu, Yingbei Tong, Anbang Xu, Rama Akkiraju
Scaling existing applications and solutions to multiple human languages has traditionally proven to be difficult, mainly due to the language-dependent nature of preprocessing and feature engineering techniques employed in traditional approaches.
no code implementations • 12 Nov 2018 • Rama Akkiraju, Vibha Sinha, Anbang Xu, Jalal Mahmud, Pritam Gundecha, Zhe Liu, Xiaotong Liu, John Schumacher
For example, existing machine learning processes cannot address how to define business use cases for an AI application, how to convert business requirements from offering managers into data requirements for data scientists, and how to continuously improve AI applications in term of accuracy and fairness, and how to customize general purpose machine learning models with industry, domain, and use case specific data to make them more accurate for specific situations etc.
no code implementations • 21 Oct 2018 • Siwei Fu, Anbang Xu, Xiaotong Liu, Huimin Zhou, Rama Akkiraju
The evaluation shows that the crowd workflow is more effective with the help of machine learning techniques.
no code implementations • EMNLP 2017 • Chenguang Wang, Alan Akbik, Laura Chiticariu, Yunyao Li, Fei Xia, Anbang Xu
Crowdsourcing has proven to be an effective method for generating labeled data for a range of NLP tasks.
no code implementations • 18 Apr 2017 • Pierre-Hadrien Arnoux, Anbang Xu, Neil Boyette, Jalal Mahmud, Rama Akkiraju, Vibha Sinha
Predicting personality is essential for social applications supporting human-centered activities, yet prior modeling methods with users written text require too much input data to be realistically used in the context of social media.
no code implementations • 17 Mar 2017 • Zhe Liu, Anbang Xu, Mengdi Zhang, Jalal Mahmud, Vibha Sinha
One problem that every presenter faces when delivering a public discourse is how to hold the listeners' attentions or to keep them involved.