Studies on Dynamic Loss Functions and Curriculum Learning in OffensEval Datasets

The spread of offensive language has become a severe social problem and may stress unmeasurable mental health illnesses. The rapid usage of social media worsens the situation. We develop a lite but robust offensive language identification system and evaluate the system on two SemEval offensive language identification shared tasks: SemEval 2019 Task 6 and SemEval 2020 Task 12. In order to take the advantage of a large semi-supervised dataset, and reduce the processing complexity of such huge data, we investigate approaches to adapt a model to the silver standards via curriculum learning and dynamic loss functions. By adapting a model to such data with the curriculum learning or dynamic loss functions, the systems are capable of scattering the focus properly on data of different difficulty levels. Experiments show both help the model learn effectively and acquire more messages from the hard cases without impairing the performance on easy cases. The best run on each task achieves competitive F1 scores of 81.6% and 91.7% on the official test data of SemEval 2019 Task 6 and SemEval 2020 Task 12 respectively with at least 50\% parameters and less data overhead, compared to the state-of-the-art systems

SBLC: a hybrid model for disease named entity recognition based on semantic bidirectional LSTMs and conditional random fields

Abstract Background Disease named entity recognition (NER) is a fundamental step in information processing of medical texts. However, disease NER involves complex issues such as descriptive modifiers in actual practice. The accurate identification of disease NER is a still an open and essential research problem in medical information extraction and text mining tasks. Methods A hybrid model named Semantics Bidirectional LSTM and CRF (SBLC) for disease named entity recognition task is proposed. The model leverages word embeddings, Bidirectional Long Short Term Memory networks and Conditional Random Fields. A publically available NCBI disease dataset is applied to evaluate the model through comparing with nine state-of-the-art baseline methods including cTAKES, MetaMap, DNorm, C-Bi-LSTM-CRF, TaggerOne and DNER. Results The results show that the SBLC model achieves an F1 score of 0.862 and outperforms the other methods. In addition, the model does not rely on external domain dictionaries, thus it can be more conveniently applied in many aspects of medical text processing. Conclusions According to performance comparison, the proposed SBLC model achieved the best performance, demonstrating its effectiveness in disease named entity recognition