Recent developments in Named Entity Recognition (NER) have resulted in better and better models. However, is there a glass ceiling? Do we know which types of errors are still hard or even impossible to correct? In this paper, we present a detailed analysis of the types of errors in state-of-the-art machine learning (ML) methods. Our study reveals the weak and strong points of the Stanford, CMU, FLAIR, ELMO and BERT models, as well as their shared limitations. We also introduce new techniques for improving annotation, for training processes and for checking a model's quality and stability.Presented results are based on the CoNLL 2003 data set for the English language. A new enriched semantic annotation of errors for this data set and new diagnostic data sets are attached in the supplementary materials.

Despite recent breakthroughs in Machine Learning for Natural Language Processing, the Natural Language Inference (NLI) problems still constitute a challenge. To this purpose, we contribute a new dataset that focuses exclusively on the factivity phenomenon; however, our task remains the same as other NLI tasks, that is prediction of entailment, contradiction, or neutral (ECN). In this paper, we describe the LingFeatured NLI corpus and present the results of analyses designed to characterize the factivity/non-factivity opposition in natural language. The dataset contains entirely natural language utterances in Polish and gathers 2432 verb-complement pairs and 309 unique verbs. The dataset is based on the National Corpus of Polish (NKJP) and is a representative subcorpus in regard to syntactic construction [V][że][cc]. We also present an extended version of the set (3035 sentences) consisting more sentences with internal negations. We prepared deep learning benchmarks for both sets. We found that transformer BERT-based models working on sentences obtained relatively good results ( $\approx 89\%$ F1 score on base dataset). Even though better results were achieved using linguistic features ( $\approx 91\%$ F1 score on base dataset), this model requires more human labor (humans in the loop) because features were prepared manually by expert linguists. BERT-based models consuming only the input sentences show that they capture most of the complexity of NLI/factivity. Complex cases in the phenomenon—for example, cases with entitlement (E) and non-factive verbs—still remain an open issue for further research.