Springer Verlag;Cham: Springer International Publishing
摘要:
摘要： Detecting named entities from documents is one of the most important tasks in knowledge engineering. Previous studies rely on annotated training data, which is quite expensive to obtain large training data sets, limiting the effectiveness of recognition. In this research, we propose a semi-supervised learning approach for named entity recognition (NER) via automatic labeling and tritraining which make use of unlabeled data and structured resources containing known named entities. By modifying tri-training for sequence labeling and deriving proper initialization, we can train a NER model for Web news articles automatically with satisfactory performance. In the task of Chinese personal name extraction from 8,672 news articles on the Web (with 364,685 sentences and 54,449 (11,856 distinct) person names), an F-measure of 90.4% can be achieved. 出版者： Cham: Springer International Publishing 出版日期： 2014 出處： Information Retrieval Technology, 2014, p.244-255 資源來源： Springer Books 版權： Springer International Publishing Switzerland 2014 識別號： ISSN: 0302-9743 識別號： ISBN: 9783319128436 識別號： ISBN: 3319128434 識別號： EISSN: 1611-3349 識別號： EISBN: 9783319128443 識別號： EISBN: 3319128442 識別號： DOI: 10.1007/978-3-319-12844-3_21
