Knowledge Adaptive Multi-Way Matching Network for Biomedical Named Entity Recognition via Machine Reading Comprehension.

Rapid and effective utilization of biomedical literature is paramount to combat diseases like COVID19. Biomedical named entity recognition (BioNER) is a fundamental task in text mining that can help physicians accelerate knowledge discovery to curb the spread of the COVID-19 epidemic. Recent approaches have shown that casting entity extraction as the machine reading comprehension task can significantly improve model performance. However, two major drawbacks impede higher success in identifying entities (1) ignoring the use of domain knowledge to capture the context beyond sentences and (2) lacking the ability to deeper understand the intent of questions. In this paper, to remedy this, we introduce and explore external domain knowledge which cannot be implicitly learned in text sequence. Previous works have focused more on text sequence and explored little of the domain knowledge. To better incorporate domain knowledge, a multi-way matching reader mechanism is devised to model representations of interaction between sequence, question and knowledge retrieved from Unified Medical Language System (UMLS). Benefiting from these, our model can better understand the intent of questions in complex contexts. Experimental results indicate that incorporating domain knowledge can help to obtain competitive results across 10 BioNER datasets, achieving absolute improvement of up to 2.02% in the f1 score.

A network representation approach for COVID-19 drug recommendation.

The coronavirus disease 2019 (COVID-19) has outbreak since early December 2019, and COVID-19 has caused over 100 million cases and 2 million deaths around the world. After one year of the COVID-19 outbreak, there is no certain and approve medicine against it. Drug repositioning has become one line of scientific research that is being pursued to develop an effective drug. However, due to the lack of COVID-19 data, there is still no specific drug repositioning targeting the COVID-19. In this paper, we propose a framework for COVID-19 drug repositioning. This framework has several advantages that can be exploited: one is that a local graph aggregating representation is used across a heterogeneous network to address the data sparsity problem; another is the multi-hop neighbors of the heterogeneous graph are aggregated to recall as many COVID-19 potential drugs as possible. Our experimental results show that our COVDR framework performs significantly better than baseline methods, and the docking simulation verifies that our three potential drugs have the ability to against COVID-19 disease.