Learning With Annotation of Various Degrees

• Published in: IEEE transaction on neural networks and learning systems Vol. 30; no. 9; pp. 2794 - 2804
• Main Authors: Zhou, Joey Tianyi, Fang, Meng, Zhang, Hao, Gong, Chen, Peng, Xi, Cao, Zhiguo, Goh, Rick Siow Mong
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.09.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Annotations; Conditional random fields; Deep conditional random field (CRF); Hidden Markov models; incomplete annotation; Labeling; Labelling; Machine learning; Neural networks; partially labeled data; Semisupervised learning; sequence labeling; Supervised learning; Training data

In this paper, we study a new problem in the scenario of sequences labeling. To be exact, we consider that the training data are with annotation of various degrees, namely, fully labeled, unlabeled, and partially labeled sequences. The learning with fully un/labeled sequence refers to the standard s...