2019/09/22 by Arijit Sehanobish, Sehanobish, Arijit, Chan Hee Song +1 · 6 citations
Computer Science · Mathematics · #Artificial Intelligence (cs.AI) #Artificial intelligence #Baseline (sea) #Computation and Language (cs.CL) #Computer science #Entity linking #F1 score #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Information retrieval #Knowledge base #Lexicographical order #Machine Learning (cs.LG) #Mathematics #Named-entity recognition #Natural Language Processing Techniques #Natural language processing #Parsing #Programming language #Set (abstract data type) #Text and Document Classification Technologies #Topic Modeling #cs.AI #cs.CL #cs.IR #cs.LG
paper · pdf · doi:10.48550/arxiv.1909.09922
published in arXiv (Cornell University) (Cornell University) · Extended abstract accepted to AAAI-2020, student track
openalex publication_date 2019/09/22 · arxiv created 2020/02/12 · arxiv updated 2020/02/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Most Named Entity Recognition (NER) systems use additional features like part-of-speech (POS) tags, shallow parsing, gazetteers, etc. Such kind of information requires external knowledge like unlabeled texts and trained taggers. Adding these features to NER systems have been shown to have a positive impact. However, sometimes creating gazetteers or taggers can take a lot of time and may require extensive data cleaning. In this paper for Chinese NER systems, we do not use these traditional features but we use lexicographic features of Chinese characters. Chinese characters are composed of graphical components called radicals and these components often have some semantic indicators. We propose CNN based models that incorporate this semantic information and use them for NER. Our models show an improvement over the baseline BERT-BiLSTM-CRF model. We set a new baseline score for Chinese OntoNotes v5.0 and show an improvement of +.64 F1 score. We present a state-of-the-art F1 score on Weibo dataset of 71.81 and show a competitive improvement of +0.72 over baseline on ResumeNER dataset.