A New Enhanced Arabic Light Stemmer for IR in Medical Documents

This paper introduces a new enhanced Arabic stemming algorithm for solving the information retrieval problem,especially in medical documents.Our proposed algorithm is a light stemming algorithm for extracting stems and roots from the input data.One of the main challenges facing the light stemming algorithm is cutting off the input word,to extract the initial segments.When initiating the light stemmer with strong initial segments,the final extracting stems and roots will be more accurate.Therefore,a new enhanced segmentation based on deploying the Direct Acyclic Graph(DAG)model is utilized.In addition to extracting the powerful initial segments,the main two procedures(i.e.,stems and roots extraction),should be also reinforced with more efficient operators to improve the final outputs.To validate the proposed enhanced stemmer,four data sets are used.The achieved stems and roots resulted from our proposed light stemmer are compared with the results obtained from five other well-known Arabic light stemmers using the same data sets.This evaluation process proved that the proposed enhanced stemmer outperformed other comparative stemmers.

Study on Multi-Label Classification of Medical Dispute Documents

The Internet of Medical Things(IoMT)will come to be of great importance in the mediation of medical disputes,as it is emerging as the core of intelligent medical treatment.First,IoMT can track the entire medical treatment process in order to provide detailed trace data in medical dispute resolution.Second,IoMT can infiltrate the ongoing treatment and provide timely intelligent decision support to medical staff.This information includes recommendation of similar historical cases,guidance for medical treatment,alerting of hired dispute profiteers etc.The multi-label classification of medical dispute documents(MDDs)plays an important role as a front-end process for intelligent decision support,especially in the recommendation of similar historical cases.However,MDDs usually appear as long texts containing a large amount of redundant information,and there is a serious distribution imbalance in the dataset,which directly leads to weaker classification performance.Accordingly,in this paper,a multi-label classification method based on key sentence extraction is proposed for MDDs.The method is divided into two parts.First,the attention-based hierarchical bi-directional long short-term memory(BiLSTM)model is used to extract key sentences from documents;second,random comprehensive sampling Bagging(RCS-Bagging),which is an ensemble multi-label classification model,is employed to classify MDDs based on key sentence sets.The use of this approach greatly improves the classification performance.Experiments show that the performance of the two models proposed in this paper is remarkably better than that of the baseline methods.

History of Chinese Medicinal Wine

Chinese medicinal wine is one type of a favorable food-drug product invented by Chinese ancestors for treating and preventing diseases, promoting people＇s health and corporeity, and enriching people＇s restorative culture. In the course of development of the millenary-old Chinese civilization, Chinese medicinal wine has made incessant progress and evolution. In different historical periods, Chinese medicinal wine presented different characteristics in basic wine medical applications, prescriptions, etc. There are many medical and Materia Medica monographs which have systemically and specifically reported on Chinese medicinal wine in past Chinese dynasties. By studying leading medical documents, this article made an outline review on the invention, development, and characteristics of Chinese medicinal wine.