Joint Biomedical Entity and Relation Extraction Based on Multi-Granularity Convolutional Tokens Pairs of Labeling

Extracting valuable information frombiomedical texts is one of the current research hotspots of concern to a wide range of scholars.The biomedical corpus contains numerous complex long sentences and overlapping relational triples,making most generalized domain joint modeling methods difficult to apply effectively in this field.For a complex semantic environment in biomedical texts,in this paper,we propose a novel perspective to perform joint entity and relation extraction;existing studies divide the relation triples into several steps or modules.However,the three elements in the relation triples are interdependent and inseparable,so we regard joint extraction as a tripartite classification problem.At the same time,fromthe perspective of triple classification,we design amulti-granularity 2D convolution to refine the word pair table and better utilize the dependencies between biomedical word pairs.Finally,we use a biaffine predictor to assist in predicting the labels of word pairs for relation extraction.Our model(MCTPL)Multi-granularity Convolutional Tokens Pairs of Labeling better utilizes the elements of triples and improves the ability to extract overlapping triples compared to previous approaches.Finally,we evaluated our model on two publicly accessible datasets.The experimental results show that our model’s ability to extract relation triples on the CPI dataset improves the F1 score by 2.34%compared to the current optimal model.On the DDI dataset,the F1 value improves the F1 value by 1.68%compared to the current optimal model.Our model achieved state-of-the-art performance compared to other baseline models in biomedical text entity relation extraction.

TG-FTIR Study on the Pyrolysis Properties of Lignin from Different Kinds of Woody Biomass

Pyrolysis properties of lignin separated from four different kinds of wood （fir, larch, poplar, and eucalyptus） compared with commercial lignin were investigated using a thermogravimetric analyzer coupled to a Fourier-transform infrared spectrometer（TG-FTIR）. Kinetic parameters of lignin thermal cracking reaction, such as activation energy and pre-exponential factor, were calculated using a three-dimensional diffusion model. The carbon residue rate and activation energy of softwood lignin were higher than those of hardwood lignin, showing that the decomposition of the former is relatively more dif？cult than that of the latter during pyrolysis. The distinct characteristic peaks of small-molecule gases and oxygenated chemicals such as ethers, phenols, acids, aldehydes, and alcohols were observed near the maximum weight loss rate by analyzing the 3D IR spectrum of the gas phase products. The formation routes of the main gaseous products were discussed, and the following order of releasing amounts was noted： CO2〉CH4〉H2O〉CO. It is believed that these results will provide valuable information for the thermo-chemical conversion process of lignin from the point of view of feedstock.