Emotion Recognition in Conversations(ERC)is fundamental in creating emotionally intelligentmachines.Graph-BasedNetwork(GBN)models have gained popularity in detecting conversational contexts for ERC tasks.However,their...Emotion Recognition in Conversations(ERC)is fundamental in creating emotionally intelligentmachines.Graph-BasedNetwork(GBN)models have gained popularity in detecting conversational contexts for ERC tasks.However,their limited ability to collect and acquire contextual information hinders their effectiveness.We propose a Text Augmentation-based computational model for recognizing emotions using transformers(TA-MERT)to address this.The proposed model uses the Multimodal Emotion Lines Dataset(MELD),which ensures a balanced representation for recognizing human emotions.Themodel used text augmentation techniques to producemore training data,improving the proposed model’s accuracy.Transformer encoders train the deep neural network(DNN)model,especially Bidirectional Encoder(BE)representations that capture both forward and backward contextual information.This integration improves the accuracy and robustness of the proposed model.Furthermore,we present a method for balancing the training dataset by creating enhanced samples from the original dataset.By balancing the dataset across all emotion categories,we can lessen the adverse effects of data imbalance on the accuracy of the proposed model.Experimental results on the MELD dataset show that TA-MERT outperforms earlier methods,achieving a weighted F1 score of 62.60%and an accuracy of 64.36%.Overall,the proposed TA-MERT model solves the GBN models’weaknesses in obtaining contextual data for ERC.TA-MERT model recognizes human emotions more accurately by employing text augmentation and transformer-based encoding.The balanced dataset and the additional training samples also enhance its resilience.These findings highlight the significance of transformer-based approaches for special emotion recognition in conversations.展开更多
Phytic acid (Myo-inositol 1,2,3,4,5,6 hexa-kisphophate) is a storage form of phosphorus and can accumulate to the levels as high as 35% in the wheat kernel. Phytic acid acts as an inhibitor for macronutrients as well ...Phytic acid (Myo-inositol 1,2,3,4,5,6 hexa-kisphophate) is a storage form of phosphorus and can accumulate to the levels as high as 35% in the wheat kernel. Phytic acid acts as an inhibitor for macronutrients as well as micronutrients and located in the bran of wheat kernel. Due to its inhibitory role, a high concentration of phytic acid is undesirable as it hinders the bio-availability of some essential nutrients such as Fe, Mg, Ca, Zn and Cu, etc. In order to check the inheritance of phytic acid in wheat kernels, phytic acid concentration was initially determined in kernels of 10 wheat genotypes to identify two contrasting genetic groups for diallel analysis. Based on pre-screening results of 10 wheat genotypes, five wheat genotypes (3 with high and 2 with low phytic acid concentration) were crossed in all possible combinations during 2007-2008 by 5 × 5 full diallel mating fashion to insight the inheritance of phytic acid and other yield contributing traits. All 20 F1 hybrids and five parental genotypes revealed significant differences statistically, except plant maturity. The narrow and broad sense heritability estimates varied widely among traits for spike length (0.17, 0.62), spikelets spike-1 (0.35, 0.74), tillers plant-1 (0.05, 0.52) and phytic acid concentration (0.01, 0.86). The values for phytic acid concentration ranged from 0.56% to 3.43% among F1 hybrids and 1.06 to 3.67% for parental genotypes. F1 hybrids, Ps-2005 × Ghaznavi (0.56%), AUP-4006 × Ps-2004 (0.74%), Janbaz × Ps-2004 (0.89%) and Janbaz × Ps-2005 (1.01%), had the lowest concentration of phytic acid. The study concluded that F1 hybrids with low phytic acid concentration could yield desirable segregants.展开更多
文摘Emotion Recognition in Conversations(ERC)is fundamental in creating emotionally intelligentmachines.Graph-BasedNetwork(GBN)models have gained popularity in detecting conversational contexts for ERC tasks.However,their limited ability to collect and acquire contextual information hinders their effectiveness.We propose a Text Augmentation-based computational model for recognizing emotions using transformers(TA-MERT)to address this.The proposed model uses the Multimodal Emotion Lines Dataset(MELD),which ensures a balanced representation for recognizing human emotions.Themodel used text augmentation techniques to producemore training data,improving the proposed model’s accuracy.Transformer encoders train the deep neural network(DNN)model,especially Bidirectional Encoder(BE)representations that capture both forward and backward contextual information.This integration improves the accuracy and robustness of the proposed model.Furthermore,we present a method for balancing the training dataset by creating enhanced samples from the original dataset.By balancing the dataset across all emotion categories,we can lessen the adverse effects of data imbalance on the accuracy of the proposed model.Experimental results on the MELD dataset show that TA-MERT outperforms earlier methods,achieving a weighted F1 score of 62.60%and an accuracy of 64.36%.Overall,the proposed TA-MERT model solves the GBN models’weaknesses in obtaining contextual data for ERC.TA-MERT model recognizes human emotions more accurately by employing text augmentation and transformer-based encoding.The balanced dataset and the additional training samples also enhance its resilience.These findings highlight the significance of transformer-based approaches for special emotion recognition in conversations.
文摘Phytic acid (Myo-inositol 1,2,3,4,5,6 hexa-kisphophate) is a storage form of phosphorus and can accumulate to the levels as high as 35% in the wheat kernel. Phytic acid acts as an inhibitor for macronutrients as well as micronutrients and located in the bran of wheat kernel. Due to its inhibitory role, a high concentration of phytic acid is undesirable as it hinders the bio-availability of some essential nutrients such as Fe, Mg, Ca, Zn and Cu, etc. In order to check the inheritance of phytic acid in wheat kernels, phytic acid concentration was initially determined in kernels of 10 wheat genotypes to identify two contrasting genetic groups for diallel analysis. Based on pre-screening results of 10 wheat genotypes, five wheat genotypes (3 with high and 2 with low phytic acid concentration) were crossed in all possible combinations during 2007-2008 by 5 × 5 full diallel mating fashion to insight the inheritance of phytic acid and other yield contributing traits. All 20 F1 hybrids and five parental genotypes revealed significant differences statistically, except plant maturity. The narrow and broad sense heritability estimates varied widely among traits for spike length (0.17, 0.62), spikelets spike-1 (0.35, 0.74), tillers plant-1 (0.05, 0.52) and phytic acid concentration (0.01, 0.86). The values for phytic acid concentration ranged from 0.56% to 3.43% among F1 hybrids and 1.06 to 3.67% for parental genotypes. F1 hybrids, Ps-2005 × Ghaznavi (0.56%), AUP-4006 × Ps-2004 (0.74%), Janbaz × Ps-2004 (0.89%) and Janbaz × Ps-2005 (1.01%), had the lowest concentration of phytic acid. The study concluded that F1 hybrids with low phytic acid concentration could yield desirable segregants.
