2016/08/24 by Sebastian Arnold, Felix A. Gers, Arnold, Sebastian +5 · 8 citations
Computer Science · #Artificial intelligence #Computation and Language (cs.CL) #Computer science #Economics #Entity linking #FOS: Computer and information sciences #Named-entity recognition #Natural Language Processing Techniques #Natural language processing #Task (project management) #Topic Modeling #Web Data Mining and Analysis #cs.CL
paper · pdf · doi:10.48550/arxiv.1608.06757
published in arXiv (Cornell University) (Cornell University) · 8 pages, 1 figure
arxiv created 2016/08/24 · openalex publication_date 2016/08/24 · arxiv updated 2016/08/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Named entity recognition often fails in idiosyncratic domains. That causes a problem for depending tasks, such as entity linking and relation extraction. We propose a generic and robust approach for high-recall named entity recognition. Our approach is easy to train and offers strong generalization over diverse domain-specific language, such as news documents (e.g. Reuters) or biomedical text (e.g. Medline). Our approach is based on deep contextual sequence learning and utilizes stacked bidirectional LSTM networks. Our model is trained with only few hundred labeled sentences and does not rely on further external knowledge. We report from our results F1 scores in the range of 84-94% on standard datasets.