Log-linear models for POS tagging were introduced by Ratnaparkhi (1996), who introduced a system called MXPOST which implemented a maximum entropy Markov model (MEMM), a slightly simpler version of a CRF. Around the same time, sequence labelers were applied to the task of named entity tagging, first with HMMs (Bikel et al., 1997) and MEMMs (McCallum et al., 2000) , and then once CRFs were developed (Lafferty et al. 2001) , they were also applied to NER (Mc-Callum and Li, 2003) . A wide exploration of features followed (Zhou et al., 2005) . Neural approaches to NER mainly follow from the pioneering results of Collobert et al. 2011, who applied a CRF on top of a convolutional net. BiLSTMs with word and character-based embeddings as input followed shortly and became a standard neural algorithm for NER (Huang et al. 2015 , Ma and Hovy 2016 , Lample et al. 2016 followed by the more recent use of Transformers and BERT.