Current state-of-the-art approaches for named entity recognition (NER) using BERT-style transformers typically use one of two different approaches: (1) The first fine-tunes the transformer itself on the NER task and adds only a simple linear layer for word-level predictions. (2) The second uses the transformer only to provide featuresto a standard LSTM-CRF sequence labeling architecture and thus performs no fine-tuning . In this paper, we perform a comparative analysis of both approaches in a variety of settings currently considered in the literature . In particular, we evaluate how well they work when document-level features are leveraged. Our evaluation on the classic CoNLL benchmark datasets for 4 languages shows that document-level features significantly improve NER quality and that fine-tuning generally outperforms the feature-based approaches . We present recommendations for parameters as well as several new state-of-the-art numbers . Our approach is integrated into the Flair framework to facilitate reproduction of our experiments.
Current state-of-the-art approaches for named entity recognition (NER) typically consider text at the sentence-level and thus do not model information that crosses sentence boundaries. However, the use of transformer-based models for NER offers natural options for capturing document-level features . In this paper, we perform a comparative evaluation of document-level features in the two standard NER architectures commonly considered in the literature , namely " fine-tuning " and " feature-based LSTM-CRF". We evaluate different hyperparameters for document-level features such as context window size and enforcing document-locality . We present experiments from which we derive recommendations for how to model document context and present new state-of-the-art scores on several CoNLL-03 benchmark datasets . Our approach is integrated into the Flair framework to facilitate reproduction of our experiments.
R
using BERT-style transformers typically use one of two different approaches: (1) The first fine-tunes the transformer itself on the NER task and adds only a simple linear layer for word-level predictions. (2) The second uses the transformer only to provide featuresto a standard LSTM-CRF sequence labeling architecture and thus performs no fine-tuning
typically consider text at the sentence-level and thus do not model information that crosses sentence boundaries. However, the use of transformer-based models for NER offers natural options for capturing document-level features
R
analysis of both approaches in a variety of settings currently
evaluation of document-level features in the two standard NER architectures commonly
R
. In particular, we evaluate how well they work when document-level features are leveraged. Our evaluation on the classic CoNLL benchmark datasets for 4 languages shows that document-level features significantly improve NER quality and that
, namely "
R
generally outperforms the
" and "
R
approaches
LSTM-CRF". We evaluate different hyperparameters for document-level features such as context window size and enforcing document-locality
R
recommendations for parameters as well as several
experiments from which we derive recommendations for how to model document context and present
R
numbers
scores on several CoNLL-03 benchmark datasets