Biomedical named entity recognition (BNER) is the basis of biomedical text mining and one of the core sub-tasks of information extraction. Previous BNER models based on conventional machine learning rely on time-consuming feature engineering. Though most neural network methods improve the problems with automatic learning, they cannot pay attention to the significant areas when capturing features. In this paper, we propose an attention-based BiLSTM-CRF model. First, this model adopts a bidirectional long short-term memory network (BiLSTM) to obtain more complete context information. At the same time, the attention mechanism is proposed to improve the vector representations in BiLSTM. We design different attention weight redistribution methods and fuse them. It effectively prevents the significant information loss when extracting features. Finally, combining BiLSTM with conditional random field (CRF) layer effectively solves the problems of the inability to handle the strong dependence of tags in the sequence. With the simple architecture, our model achieves a reasonable performance on the JNLPBA corpus. It obtains an F1-score of 73.50. Our model can enhance the ability of the neural network to extract significant information and does not rely on any feature engineering, with only general pre-training word vectors. It makes our model have high portability and extendibility.INDEX TERMS Biomedical text, named entity recognition, attention mechanism, long short-term memory, conditional random field.
As the biomedical literature increases exponentially, biomedical named entity recognition (BNER) has become an important task in biomedical information extraction. In the previous studies based on deep learning, pretrained word embedding becomes an indispensable part of the neural network models, effectively improving their performance. However, the biomedical literature typically contains numerous polysemous and ambiguous words. Using fixed pretrained word representations is not appropriate. Therefore, this paper adopts the pretrained embeddings from language models (ELMo) to generate dynamic word embeddings according to context. In addition, in order to avoid the problem of insufficient training data in specific fields and introduce richer input representations, we propose a multitask learning multichannel bidirectional gated recurrent unit (BiGRU) model. Multiple feature representations (e.g., word-level, contextualized word-level, character-level) are, respectively, or collectively fed into the different channels. Manual participation and feature engineering can be avoided through automatic capturing features in BiGRU. In merge layer, multiple methods are designed to integrate the outputs of multichannel BiGRU. We combine BiGRU with the conditional random field (CRF) to address labels’ dependence in sequence labeling. Moreover, we introduce the auxiliary corpora with same entity types for the main corpora to be evaluated in multitask learning framework, then train our model on these separate corpora and share parameters with each other. Our model obtains promising results on the JNLPBA and NCBI-disease corpora, with F1-scores of 76.0% and 88.7%, respectively. The latter achieves the best performance among reported existing feature-based models.
Authorship profiling is a subtask of authorship identification. This task can be regarded as an analysis of personal writing styles, which has been widely investigated. However, no previous studies have attempted to analyze the authorship of classical Chinese poetry. First, we provide an approach to evaluate the popularity of poets, and we also establish a public corpus containing the top 20 most popular poets in the Tang Dynasty for authorship profiling. Then, a novel poetry authorship profiling framework named multidimensional domain knowledge poet profiling (M-DKPP) is proposed, combining the knowledge of authorship attribution and the text’s stylistic features with domain knowledge described by experts in traditional poetry studies. A case study for Li Bai is used to prove the validity and applicability of our framework. Finally, the performance of M-DKPP framework is evaluated with four poem datasets. On all datasets, the proposed framework outperforms several baseline approaches for authorship attribution.
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