Active Annotation

@article{vlachos2006aa, abstract = {This paper introduces a semi-supervised learning framework for creating training material, namely active annotation. The main intuition is that an unsupervised method is used to initially annotate imper- fectly the data and then the errors made are detected automatically and corrected by a human annotator. We applied ac- tive annotation to named entity recogni- tion in the biomedical domain and encour- aging results were obtained. The main advantages over the popular active learn- ing framework are that no seed annotated data is needed and that the reusability of the data is maintained. In addition to the framework, an efficient uncertainty esti- mation for Hidden Markov Models is pre- sented}, author = {Vlachos, A. }, citeulike-article-id = {1366612}, journal = {Proceedings of the EACL 2006 Workshop on Adaptive Text Extraction}, keywords = {active-annotation, annotations, machine\_learning--semi-supervised, named-entity-recognition, training-data}, posted-at = {2007-06-06 00:07:59}, priority = {0}, title = {Active Annotation}, url = {http://acl.ldc.upenn.edu/W/W06/W06-2209.pdf}, year = {2006} }

TY - JOUR ID - vlachos2006aa L3 - citeulike-article-id:1366612 TI - Active Annotation JF - Proceedings of the EACL 2006 Workshop on Adaptive Text Extraction N2 - This paper introduces a semi-supervised learning framework for creating training material, namely active annotation. The main intuition is that an unsupervised method is used to initially annotate imper- fectly the data and then the errors made are detected automatically and corrected by a human annotator. We applied ac- tive annotation to named entity recogni- tion in the biomedical domain and encour- aging results were obtained. The main advantages over the popular active learn- ing framework are that no seed annotated data is needed and that the reusability of the data is maintained. In addition to the framework, an efficient uncertainty esti- mation for Hidden Markov Models is pre- sented KW - active-annotation KW - annotations KW - machine_learning--semi-supervised KW - named-entity-recognition KW - training-data AU - Vlachos, A PY - 2006/// UR - http://acl.ldc.upenn.edu/W/W06/W06-2209.pdf ER -