S-NER: A Concise and Efficient Span-Based Model for Named Entity Recognition.Named entity recognition (NER) is a task that seeks to recognize entities in raw texts and is a precondition for a series of downstream NLP tasks.  To address the above issue, we propose S-NER, a span-based NER model.  To be specific, S-NER first splits raw texts into text spans and regards them as candidate entities; it then directly obtains the types of spans by conducting entity type classifications on span semantic representations, which eliminates the requirement for label dependency.  Moreover, S-NER has a concise neural architecture in which it directly uses BERT as its encoder and a feed-forward network as its decoder.  We evaluate S-NER on several benchmark datasets across three domains.  Experimental results demonstrate that S-NER consistently outperforms the strongest baselines in terms of F1-score.  Extensive analyses further confirm the efficacy of S-NER.
S-NER
S-NER: A Concise and Efficient Span-Based Model for Named Entity Recognition.Named entity recognition (NER) is a task that seeks to recognize entities in raw texts and is a precondition for a series of downstream NLP tasks.  To address the above issue, we propose S-NER, a span-based NER model.  To be specific, S-NER first splits raw texts into text spans and regards them as candidate entities; it then directly obtains the types of spans by conducting entity type classifications on span semantic representations, which eliminates the requirement for label dependency.  Moreover, S-NER has a concise neural architecture in which it directly uses BERT as its encoder and a feed-forward network as its decoder.  We evaluate S-NER on several benchmark datasets across three domains.  Experimental results demonstrate that S-NER consistently outperforms the strongest baselines in terms of F1-score.  Extensive analyses further confirm the efficacy of S-NER.
S-NER
Moreover, S-NER has a concise neural architecture in which it directly uses BERT as its encoder and a feed-forward network as its decoder. 
BERT
Traditionally, prior NER models use the sequence labeling mechanism which requires label dependency captured by the conditional random fields (CRFs). 
CRFs
S-NER: A Concise and Efficient Span-Based Model for Named Entity Recognition.Named entity recognition (NER) is a task that seeks to recognize entities in raw texts and is a precondition for a series of downstream NLP tasks.  To address the above issue, we propose S-NER, a span-based NER model.  To be specific, S-NER first splits raw texts into text spans and regards them as candidate entities; it then directly obtains the types of spans by conducting entity type classifications on span semantic representations, which eliminates the requirement for label dependency.  Moreover, S-NER has a concise neural architecture in which it directly uses BERT as its encoder and a feed-forward network as its decoder.  We evaluate S-NER on several benchmark datasets across three domains.  Experimental results demonstrate that S-NER consistently outperforms the strongest baselines in terms of F1-score.  Extensive analyses further confirm the efficacy of S-NER.
S-NER