Nested Named Entity Recognition via Second-best Sequence Learning and Decoding
Table 9: Comparison on CoNLL-2003. We group methods into two types. The first group consists of the methods that do not use any contextual word embeddings. The second one consists of the methods that use contextual word embeddings such as BERT and FLAIR. “†” indicates the methods using POS tags. “‡” indicates the methods not designed to extract nested entities.
['[BOLD] Method', 'F1 (%)']
First, in comparison with the methods designed for nested NER Wang and Lu This means that our method works well on not only nested NER but also flat NER. Next, we compare to methods that can handle only flat NER. However, note that there are some differences between the experiments of the previous studies Lample et al. For example, different word embeddings are used, or the hidden size of LSTM is not aligned. Nevertheless, we can compare our proposed model to the ablated model. p<0.005 with the permutation test) between the two scores, 91.14(±0.04)% and 90.84(±0.10)%. We analyze this gap in detail and then find out that our proposed model performs well especially in the cases where it is difficult to decide which is suitable, an inner span or an outer span. Given the following sentence: “An assessment group made up of the State Council ’s Port Office , the Civil Aviation Administration of China , the General Administration of Customs and other authorities had granted the airport permission to handle foreign aircraft , Xinhua said .”. In the CoNLL-2003 dataset, the four spans “State Council”, “Civil Aviation Administration of China”, “General Administration of Customs”, and “Xinhua” are annotated as ORG (Organization). The both models correctly detect the latter three entities in most trials, but the ablated model tends to extract “State Council ’s Port Office” instead of “State Council”. On the other hand, our proposed model tends to extract both “State Council ’s Port Office” and “State Council”. “State Council ’s Port Office” is indeed a false-positive, but our model can detect the correct entity span “State Council” more steadily than the ablated model. Thus, our proposed model achieves the higher F1-score.