Automatic Keyphrase Extraction from Scientific Chinese Medical Abstracts Based on Character-Level Sequence Labeling

Purpose:Automatic keyphrase extraction(AKE)is an important task for grasping the main points of the text.In this paper,we aim to combine the benefits of sequence labeling formulation and pretrained language model to propose an automatic keyphrase extraction model for Chinese scientific research.Design/methodology/approach:We regard AKE from Chinese text as a character-level sequence labeling task to avoid segmentation errors of Chinese tokenizer and initialize our model with pretrained language model BERT,which was released by Google in 2018.We collect data from Chinese Science Citation Database and construct a large-scale dataset from medical domain,which contains 100,000 abstracts as training set,6,000 abstracts as development set and 3,094 abstracts as test set.We use unsupervised keyphrase extraction methods including term frequency(TF),TF-IDF,TextRank and supervised machine learning methods including Conditional Random Field(CRF),Bidirectional Long Short Term Memory Network(BiLSTM),and BiLSTM-CRF as baselines.Experiments are designed to compare word-level and character-level sequence labeling approaches on supervised machine learning models and BERT-based models.Findings:Compared with character-level BiLSTM-CRF,the best baseline model with F1 score of 50.16%,our character-level sequence labeling model based on BERT obtains F1 score of 59.80%,getting 9.64%absolute improvement.Research limitations:We just consider automatic keyphrase extraction task rather than keyphrase generation task,so only keyphrases that are occurred in the given text can be extracted.In addition,our proposed dataset is not suitable for dealing with nested keyphrases.Practical implications:We make our character-level IOB format dataset of Chinese Automatic Keyphrase Extraction from scientific Chinese medical abstracts(CAKE)publicly available for the benefits of research community,which is available at:https://github.com/possible1402/Dataset-For-Chinese-Medical-Keyphrase-Extraction.Originality/value:By designing comparative experiments,our study demonstrates that character-level formulation is more suitable for Chinese automatic keyphrase extraction task under the general trend of pretrained language models.And our proposed dataset provides a unified method for model evaluation and can promote the development of Chinese automatic keyphrase extraction to some extent.

Chrysophanol attenuates lead exposure-induced injury to hippocampal neurons in neonatal mice

Previous studies have shown that chrysophanol protects against learning and memory impairments in lead-exposed adult mice. In the present study, we investigated whether chrysophanol can alleviate learning and memory dysfunction and hippocampal neuronal injury in lead-exposed neonatal mice. At the end of lactation, chrysophanol（0.1, 1.0, 10.0 mg/kg） was administered to the neonatal mice by intraperitoneal injection for 15 days. Chrysophanol significantly alleviated injury to hippocampal neurons and improved learning and memory abilities in the lead-poisoned neonatal mice. Chrysophanol also significantly decreased lead content in blood, brain, heart, spleen, liver and kidney in the lead-exposed neonatal mice. The levels of malondialdehyde in the brain, liver and kidney were significantly reduced, and superoxide dismutase and glutathione peroxidase activities were significantly increased after chrysophanol treatment. Collectively, these findings indicate that chrysophanol can significantly reduce damage to hippocampal neurons in lead-exposed neonatal mice.