2018/11/26 by Sławomir Dadas · 7 citations
Computer Science · Decision Sciences · #Advanced Graph Neural Networks #Artificial neural network #Conditional random field #Data Quality and Management #Entity linking #Feature (linguistics) #Inference #Named entity #Named-entity recognition #Topic Modeling #Word (group theory) #Word error rate #cs.CL
paper · pdf · doi:10.1007/978-3-030-20912-4_4
published in Lecture notes in computer science, 39-50 (Springer Science+Business Media)
arxiv created 2018/11/26 · openalex created_date 2018/11/29 · openalex publication_date 2019/01/01 · arxiv updated 2019/06/07 · openalex updated_date 2026/08/05
Named entity recognition (NER) is one of the tasks in natural language processing that can greatly benefit from the use of external knowledge sources. We propose a named entity recognition framework composed of knowledge-based feature extractors and a deep learning model including contextual word embeddings, long short-term memory (LSTM) layers and conditional random fields (CRF) inference layer. We use an entity linking module to integrate our system with Wikipedia. The combination of effective neural architecture and external resources allows us to obtain state-of-the-art results on recognition of Polish proper names. We evaluate our model on data from PolEval 2018 NER challenge on which it outperforms other methods, reducing the error rate by 22.4% compared to the winning solution. Our work shows that combining neural NER model and entity linking model with a knowledge base is more effective in recognizing named entities than using NER model alone.