Modeling Multi-Targets Sentiment Classification via Graph Convolutional Networks and Auxiliary Relation

Existing solutions do not work well when multi-targets coexist in a sentence. The reason is that the existing solution is usually to separate multiple targets and process them separately. If the original sentence has N target, the original sentence will be repeated for N times, and only one target will be processed each time. To some extent, this approach degenerates the fine-grained sentiment classification task into the sentencelevel sentiment classification task, and the research method of processing the target separately ignores the internal relation and interaction between the targets. Based on the above considerations, we proposes to use Graph Convolutional Network (GCN) to model and process multi-targets appearing in sentences at the same time based on the positional relationship, and then to construct a graph of the sentiment relationship between targets based on the difference of the sentiment polarity between target words. In addition to the standard target-dependent sentiment classification task, an auxiliary node relation classification task is constructed. Experiments demonstrate that our model achieves new comparable performance on the benchmark datasets: SemEval-2014 Task 4, i.e., reviews for restaurants and laptops. Furthermore, the method of dividing the target words into isolated individuals has disadvantages, and the multi-task learning model is beneficial to enhance the feature extraction ability and expression ability of the model

Effect of Oak Chip Aging on the Flavor of Persimmon Brandy

Mopan persimmon brandy with an alcohol content of 42% (V/V), prepared by fermentation and distillation, was aged after being added with 5–20 g/L of Chinese-made moderately roasted oak chips. The volatile and non-volatile components of persimmon brandy were analyzed by gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS), the total phenol content and antioxidant activity were determined, and sensory evaluation was also performed. The results showed that a total of 33 volatile components were identified by GC-MS, among which the major components were ethyl acetate, ethyl decanoate, and ethyl laurate. The content of volatile components was the highest upon the addition of 10 g/L of oak chips. The results of LC-MS showed that the number of non-volatile substances increased by 183 after aging. The total phenol content and 1,1-diphenyl-2-picrylhydrazyl (DPPH) radical scavenging capacity increased with increasing addition of oak chips, but was basically stable after 90 days of aging. In the sensory evaluation, persimmon brandy with 15 g/L of oak chip scored the highest (72.5 points)

Multidimensional Self-Attention for Aspect Term Extraction and Biomedical Named Entity Recognition

Wide attention has been paid to named entity recognition (NER) in specific fields. Among the representative tasks are the aspect term extraction (ATE) in user online comments and the biomedical named entity recognition (BioNER) in medical documents. Existing methods only perform well in a particular field, and it is difficult to maintain an advantage in other fields. In this article, we propose a supervised learning method that can be used for much special domain NER tasks. The model consists of two parts, a multidimensional self-attention (MDSA) network and a CNN-based model. The multidimensional self-attention mechanism can calculate the importance of the context to the current word, select the relevance according to the importance, and complete the update of the word vector. This update mechanism allows the subsequent CNN model to have variable-length memory of sentence context. We conduct experiments on benchmark datasets of ATE and BioNER tasks. The results show that our model surpasses most baseline methods

Food cue recruits increased reward processing and decreased inhibitory control processing in the obese/overweight: An activation C likelihood estimation meta-analysis of fMRI studies

Introduction: Growing researches have shown that obese/overweight and healthy weight individuals exhibit different neural responses to food-related stimuli. Accordingly, researchers proposed several theories to explain these differences. Hereon, meta-analyses were conducted using activation likelihood estimation (ALE) to verify these theories and specify the reason of overeating from two aspects. Materials and Methods: Pubmed, Web of Science and Neurosynth were searched for the current study and screened according to inclusion criteria. Firstly, neural responses to visual food cues versus non-food images were compared between obese/overweight and healthy weight individuals. Then, neural activation to high-calorie food images versus low-calorie food/non-food visual stimuli was further investigated among the two populations. Coordinates in included studies were recorded and analysed by Ginger ALE software under threshold at uncorrected p < 0.001 with cluster-level p < 0.05 (cFWE). Results: Eleven and seven studies were found in the first and second set of meta-analysis, respectively. The first meta-analysis showed that obese/overweight have hyper-responsivity in reward area and hyporesponsivity in both gustatory processing and inhibitory control area. The second meta-analysis indicated that the reward responsivity in the obese/overweight individuals was amplified and healthy weight individuals had higher activation in areas associated with gustatory processing in response to high-calorie food images. Conclusions: Our results showed that the obese/overweight exhibit hyper-responsivity in brain regions involved in reward processing for visual food cue which provide strong support for incentive-sensitization theory of obesity and healthy weight individuals showed higher response in inhibitory control region which support the inhibitory control deficit theory of obesity. (C) 2020 Asia Oceania Association for the Study of Obesity. Published by Elsevier Ltd. All rights reserved