Exploiting Language Models to Classify Events from Twitter

Classifying events is challenging in Twitter because tweets texts have a large amount of temporal data with a lot of noise and various kinds of topics. In this paper, we propose a method to classify events from Twitter. We firstly find the distinguishing terms between tweets in events and measure their similarities with learning language models such as ConceptNet and a latent Dirichlet allocation method for selectional preferences (LDA-SP), which have been widely studied based on large text corpora within computational linguistic relations. The relationship of term words in tweets will be discovered by checking them under each model. We then proposed a method to compute the similarity between tweets based on tweets' features including common term words and relationships among their distinguishing term words. It will be explicit and convenient for applying to k-nearest neighbor techniques for classification. We carefully applied experiments on the Edinburgh Twitter Corpus to show that our method achieves competitive results for classifying events

Extracting temporal and causal relations based on event networks

© 2020 Elsevier Ltd Event relations specify how different event flows expressed within the context of a textual passage relate to each other in terms of temporal and causal sequences. There have already been impactful work in the area of temporal and causal event relation extraction; however, the challenge with these approaches is that (1) they are mostly supervised methods and (2) they rely on syntactic and grammatical structure patterns at the sentence-level. In this paper, we address these challenges by proposing an unsupervised event network representation for temporal and causal relation extraction that operates at the document level. More specifically, we benefit from existing Open IE systems to generate a set of triple relations that are then used to build an event network. The event network is bootstrapped by labeling the temporal disposition of events that are directly linked to each other. We then systematically traverse the event network to identify the temporal and causal relations between indirectly connected events. We perform experiments based on the widely adopted TempEval-3 and Causal-TimeBank corpora and compare our work with several strong baselines. We show that our method improves performance compared to several strong methods

Practice skills and compliance of private pharmacies with regulations on the prescription drug: A multi-method study in Vietnam

Professional practice of pharmacists plays a crucial role in the reinforcement of drug retailers’ services to achieve optimal health care provision to customers. To evaluate the professional skills and compliance of retail pharmacy staff with selling prescription drugs by surveying patients’ knowledge of drugs and role-playing the customer buying antibiotics without a prescription. A cross-sectional study was conducted with two kinds of surveys at 480 drug retail establishments using the cluster sample technique among 12 provinces/cities in Vietnam. Clients were interviewed to assess their knowledge about drugs. Moreover, the method of acting as a client was used in two common scenarios in order to evaluate the implementation of professional regulations and professional practice skills of drug sellers: a child acute respiratory infection (ARI) case and an amoxicillin case without a prescription. The data were presented as frequency and percentage. The basic tests were used to compare the ratios and means between the two groups. The total number of interviewed customers was 2389 while the figure for role-playing was 960 cases. When customers buy medications with a prescription, 100% of those were fully aware of the dosage of the drugs they purchased, which was higher than the scenario of buying without a prescription (93.1%). In role-play scenarios, the rate of drug sellers asking patients to explore information was higher in the ARI children case than in the amoxicillin case. Besides, 100% of customers were consulted on treatment in both cases, and the rate of advising was at a low rate in both scenarios 3.8% in the amoxicillin case compared to 15.4% in the ARI case. Drug sellers did not respond well to requirements in professional practice and were influenced by economic concerns in business

Exploiting Language Models to Classify Events from Twitter

Classifying events is challenging in Twitter because tweets texts have a large amount of temporal data with a lot of noise and various kinds of topics. In this paper, we propose a method to classify events from Twitter. We firstly find the distinguishing terms between tweets in events and measure their similarities with learning language models such as ConceptNet and a latent Dirichlet allocation method for selectional preferences (LDA-SP), which have been widely studied based on large text corpora within computational linguistic relations. The relationship of term words in tweets will be discovered by checking them under each model. We then proposed a method to compute the similarity between tweets based on tweets’ features including common term words and relationships among their distinguishing term words. It will be explicit and convenient for applying to k-nearest neighbor techniques for classification. We carefully applied experiments on the Edinburgh Twitter Corpus to show that our method achieves competitive results for classifying events

Practice skills and compliance of private pharmacies with regulations on the prescription drug: A multi-method study in Vietnam

Professional practice of pharmacists plays a crucial role in the reinforcement of drug retailers’ services to achieve optimal health care provision to customers. To evaluate the professional skills and compliance of retail pharmacy staff with selling prescription drugs by surveying patients’ knowledge of drugs and role-playing the customer buying antibiotics without a prescription. A cross-sectional study was conducted with two kinds of surveys at 480 drug retail establishments using the cluster sample technique among 12 provinces/cities in Vietnam. Clients were interviewed to assess their knowledge about drugs. Moreover, the method of acting as a client was used in two common scenarios in order to evaluate the implementation of professional regulations and professional practice skills of drug sellers: a child acute respiratory infection (ARI) case and an amoxicillin case without a prescription. The data were presented as frequency and percentage. The basic tests were used to compare the ratios and means between the two groups. The total number of interviewed customers was 2389 while the figure for role-playing was 960 cases. When customers buy medications with a prescription, 100% of those were fully aware of the dosage of the drugs they purchased, which was higher than the scenario of buying without a prescription (93.1%). In role-play scenarios, the rate of drug sellers asking patients to explore information was higher in the ARI children case than in the amoxicillin case. Besides, 100% of customers were consulted on treatment in both cases, and the rate of advising was at a low rate in both scenarios 3.8% in the amoxicillin case compared to 15.4% in the ARI case. Drug sellers did not respond well to requirements in professional practice and were influenced by economic concerns in business

Scalable Fabrication of Modified Graphene Nanoplatelets as an Effective Additive for Engine Lubricant Oil

The use of nano-additives is widely recognized as a cheap and effective pathway to improve the performance of lubrication by minimizing the energy loss from friction and wear, especially in diesel engines. In this work, a simple and scalable protocol was proposed to fabricate a graphene additive to improve the engine lubricant oil. Graphene nanoplates (GNPs) were obtained by a one-step chemical exfoliation of natural graphite and were successfully modified with a surfactant and an organic compound to obtain a modified GNP additive, that can be facilely dispersed in lubricant oil. The GNPs and modified GNP additive were characterized using scanning electron microscopy, X-ray diffraction, atomic force microscopy, Raman spectroscopy, and Fourier-transform infrared spectroscopy. The prepared GNPs had wrinkled and crumpled structures with a diameter of 10–30 µm and a thickness of less than 15 nm. After modification, the GNP surfaces were uniformly covered with the organic compound. The addition of the modified GNP additive to the engine lubricant oil significantly enhanced the friction and antiwear performance. The highest reduction of 35% was determined for the wear scar diameter with a GNP additive concentration of approximately 0.05%. The mechanism for lubrication enhancement by graphene additives was also briefly discussed

Structural Characterization and Cytotoxic Activity Evaluation of Ulvan Polysaccharides Extracted from the Green Algae <i>Ulva papenfussii</i>

Ulvan, a sulfated heteropolysaccharide with structural and functional properties of interest for various uses, was extracted from the green seaweed Ulva papenfussii. U. papenfussii is an unexplored Ulva species found in the South China Sea along the central coast of Vietnam. Based on dry weight, the ulvan yield was ~15% (w/w) and the ulvan had a sulfate content of 13.4 wt%. The compositional constitution encompassed L-Rhamnose (Rhap), D-Xylose (Xylp), D-Glucuronic acid (GlcAp), L-Iduronic acid (IdoAp), D-Galactose (Galp), and D-Glucose (Glcp) with a molar ratio of 1:0.19:0.35:0.52:0.05:0.11, respectively. The structure of ulvan was determined using High-Performance Liquid Chromatography (HPLC), Fourier Transform Infrared Spectroscopy (FT-IR), and Nuclear Magnetic Resonance spectroscopy (NMR) methods. The results showed that the extracted ulvan comprised a mixture of two different structural forms, namely (“A3s”) with the repeating disaccharide [→4)-β-D-GlcAp-(1→4)-α-L-Rhap 3S-(1→]n, and (“B3s”) with the repeating disaccharide [→4)-α-L-IdoAp-(1→4)-α-L-Rhap 3S(1→]n. The relative abundance of A3s, and B3s was 1:1.5, respectively. The potential anticarcinogenic attributes of ulvan were evaluated against a trilogy of human cancer cell lineages. Concomitantly, Quantitative Structure–Activity Relationship (QSAR) modeling was also conducted to predict potential adverse reactions stemming from pharmacological interactions. The ulvan showed significant antitumor growth activity against hepatocellular carcinoma (IC50 ≈ 90 µg/mL), human breast cancer cells (IC50 ≈ 85 µg/mL), and cervical cancer cells (IC50 ≈ 67 µg/mL). The QSAR models demonstrated acceptable predictive power, and seven toxicity indications confirmed the safety of ulvan, warranting its candidacy for further in vivo testing and applications as a biologically active pharmaceutical source for human disease treatment