基於已知名稱搜尋結果的網路實體辨識模型建立工具(A Tool for Web NER Model Generation Using Search Snippets of Known Entities) [In Chinese]
黃雅筠 Ya-Yun Huang 國立中央大學資訊工程學系 Department of Computer Science and Information Engineering National Central University a2425320032002@gmail.com 張嘉惠 Chia-Hui Chang 國立中央大學資訊工程學系 Department of Computer Science and Information Engineering National Central University chia@csie.ncu.edu.tw 周建龍 Chia-Hui Chang 國立中央大學資訊工程學系 Department of Computer Science and Information Engineering National Central University formatc.chou@gmail.com
Semi-supervised Sequence Labeling for Named Entity Extraction based on Tri-Training: Case Study on Chinese Person Name Extraction
Named entity extraction is a fundamental task for many knowledge engineering applications. Existing studies rely on annotated training data, which is quite expensive when used to obtain large data sets, limiting the effectiveness of recognition. In this research, we propose an automatic labeling procedure to prepare training data from structured resources which contain known named entities. While this automatically labeled training data may contain noise, a self-testing procedure may be used as a follow-up to remove low-confidence annotation and increase the extraction performance with less training data. In addition to the preparation of labeled training data, we also employed semi-supervised learning to utilize large unlabeled training data. By modifying tri-training for sequence labeling and deriving the proper initialization, we can further improve entity extraction. In the task of Chinese personal name extraction with 364,685 sentences (8,672 news articles) and 54,449 (11,856 distinct) person names, an F-measure of 90.4% can be achieved.
Memory-based one-step named-entity recognition: Effects of seed list features, classifier stacking, and unannotated data
We present a memory-based named-entity recognition system that chunks and labels named entities in a oneshot task. Training and testing on CoNLL-2003 shared task data, we measure the effects of three extensions. First, we incorporate features that signal the presence of wordforms in external, language-specific seed (gazetteer) lists. Second, we build a second-stage stacked classifier that corrects first-stage output errors. Third, we add selected instances from classified unannotated data to the training material. The system that incorporates all attains an overall F-rate on the final test set of 78.20 on English and 63.02 on German.