scholarly journals DialogueRNN: An Attentive RNN for Emotion Detection in Conversations

2019 ◽  
Vol 33 ◽  
pp. 6818-6825 ◽  
Author(s):  
Navonil Majumder ◽  
Soujanya Poria ◽  
Devamanyu Hazarika ◽  
Rada Mihalcea ◽  
Alexander Gelbukh ◽  
...  
Keyword(s):  
Neural Networks ◽  
Social Media ◽  
Recurrent Neural Networks ◽  
Opinion Mining ◽  
State Of The Art ◽  
Emotion Detection ◽  
Emotion Classification ◽  
Argumentation Mining ◽  
Consumer Feedback ◽  
The Individual

Emotion detection in conversations is a necessary step for a number of applications, including opinion mining over chat history, social media threads, debates, argumentation mining, understanding consumer feedback in live conversations, and so on. Currently systems do not treat the parties in the conversation individually by adapting to the speaker of each utterance. In this paper, we describe a new method based on recurrent neural networks that keeps track of the individual party states throughout the conversation and uses this information for emotion classification. Our model outperforms the state-of-the-art by a significant margin on two different datasets.

Levenshtein Augmentation Improves Performance of SMILES Based Deep-Learning Synthesis Prediction

2020 ◽  
Author(s):  
Dean Sumner ◽  
Jiazhen He ◽  
Amol Thakkar ◽  
Ola Engkvist ◽  
Esben Jannik Bjerrum
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Deep Learning ◽  
Recurrent Neural Networks ◽  
Data Augmentation ◽  
State Of The Art ◽  
Sequence Similarity ◽  
Learning Models ◽  
Underlying Network

<p>SMILES randomization, a form of data augmentation, has previously been shown to increase the performance of deep learning models compared to non-augmented baselines. Here, we propose a novel data augmentation method we call “Levenshtein augmentation” which considers local SMILES sub-sequence similarity between reactants and their respective products when creating training pairs. The performance of Levenshtein augmentation was tested using two state of the art models - transformer and sequence-to-sequence based recurrent neural networks with attention. Levenshtein augmentation demonstrated an increase performance over non-augmented, and conventionally SMILES randomization augmented data when used for training of baseline models. Furthermore, Levenshtein augmentation seemingly results in what we define as <i>attentional gain </i>– an enhancement in the pattern recognition capabilities of the underlying network to molecular motifs.</p>

Convolutional Nonlinear Differential Recurrent Neural Networks for Crowd Scene Understanding

2018 ◽  
Vol 12 (04) ◽  
pp. 481-500 ◽  
Author(s):  
Naifan Zhuang ◽  
The Duc Kieu ◽  
Jun Ye ◽  
Kien A. Hua
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Short Term Memory ◽  
Scene Understanding ◽  
Image Data ◽  
High Density ◽  
Temporal Information ◽  
Deep Model ◽  
End To End ◽  
The Individual

With the growth of crowd phenomena in the real world, crowd scene understanding is becoming an important task in anomaly detection and public security. Visual ambiguities and occlusions, high density, low mobility, and scene semantics, however, make this problem a great challenge. In this paper, we propose an end-to-end deep architecture, convolutional nonlinear differential recurrent neural networks (CNDRNNs), for crowd scene understanding. CNDRNNs consist of GoogleNet Inception V3 convolutional neural networks (CNNs) and nonlinear differential recurrent neural networks (RNNs). Different from traditional non-end-to-end solutions which separate the steps of feature extraction and parameter learning, CNDRNN utilizes a unified deep model to optimize the parameters of CNN and RNN hand in hand. It thus has the potential of generating a more harmonious model. The proposed architecture takes sequential raw image data as input, and does not rely on tracklet or trajectory detection. It thus has clear advantages over the traditional flow-based and trajectory-based methods, especially in challenging crowd scenarios of high density and low mobility. Taking advantage of CNN and RNN, CNDRNN can effectively analyze the crowd semantics. Specifically, CNN is good at modeling the semantic crowd scene information. On the other hand, nonlinear differential RNN models the motion information. The individual and increasing orders of derivative of states (DoS) in differential RNN can progressively build up the ability of the long short-term memory (LSTM) gates to detect different levels of salient dynamical patterns in deeper stacked layers modeling higher orders of DoS. Lastly, existing LSTM-based crowd scene solutions explore deep temporal information and are claimed to be “deep in time.” Our proposed method CNDRNN, however, models the spatial and temporal information in a unified architecture and achieves “deep in space and time.” Extensive performance studies on the Violent-Flows, CUHK Crowd, and NUS-HGA datasets show that the proposed technique significantly outperforms state-of-the-art methods.

Real-Time Streaming Data Analysis Using a Three-Way Classification Method for Sentimental Analysis

2018 ◽  
Vol 13 (3) ◽  
pp. 99-111 ◽  
Author(s):  
Srinidhi Hiriyannaiah ◽  
G.M. Siddesh ◽  
K.G. Srinivasa
Keyword(s):  
Social Media ◽  
Opinion Mining ◽  
Streaming Data ◽  
Classification Method ◽  
Emotion Classification ◽  
Knowledge Based ◽  
Twitter Data ◽  
Internet Users ◽  
Word Classification ◽  
Three Stages

This article describes how recent advances in computing have led to an increase in the generation of data in fields such as social media, medical, power and others. With the rapid increase in internet users, social media has given power for sentiment analysis or opinion mining. It is a highly challenging task for storing, querying and analyzing such types of data. This article aims at providing a solution to store, query and analyze streaming data using Apache Kafka as the platform and twitter data as an example for analysis. A three-way classification method is proposed for sentimental analysis of twitter data that combines both the approaches for knowledge-based and machine-learning using three stages namely emotion classification, word classification and sentiment classification. The hybrid three-way classification approach was evaluated using a sample of five query strings on twitter and compared with existing emotion classifier, polarity classifier and Naïve Bayes classifier for sentimental analysis. The accuracy of the results of the proposed approach is superior when compared to existing approaches.

scholarly journals Accurate and efficient time-domain classification with adaptive spiking recurrent neural networks

2021 ◽  
Author(s):  
Bojian Yin ◽  
Federico Corradi ◽  
Sander M. Bohté
Keyword(s):  
Neural Networks ◽  
Time Domain ◽  
Recurrent Neural Networks ◽  
State Of The Art ◽  
Spiking Neurons ◽  
Recurrent Networks ◽  
Computationally Efficient ◽  
Hardware Implementations ◽  
Comparable Performance ◽  
The Time Domain

ABSTRACTInspired by more detailed modeling of biological neurons, Spiking neural networks (SNNs) have been investigated both as more biologically plausible and potentially more powerful models of neural computation, and also with the aim of extracting biological neurons’ energy efficiency; the performance of such networks however has remained lacking compared to classical artificial neural networks (ANNs). Here, we demonstrate how a novel surrogate gradient combined with recurrent networks of tunable and adaptive spiking neurons yields state-of-the-art for SNNs on challenging benchmarks in the time-domain, like speech and gesture recognition. This also exceeds the performance of standard classical recurrent neural networks (RNNs) and approaches that of the best modern ANNs. As these SNNs exhibit sparse spiking, we show that they theoretically are one to three orders of magnitude more computationally efficient compared to RNNs with comparable performance. Together, this positions SNNs as an attractive solution for AI hardware implementations.

scholarly journals An empirical study on temporal modeling for online action detection

2021 ◽  
Author(s):  
Wen Wang ◽  
Xiaojiang Peng ◽  
Yu Qiao ◽  
Jian Cheng
Keyword(s):  
Neural Networks ◽  
Empirical Study ◽  
Recurrent Neural Networks ◽  
State Of The Art ◽  
Deep Convolutional Neural Networks ◽  
Temporal Modeling ◽  
Action Detection ◽  
Modeling Methods ◽  
Feature Extractor ◽  
First Time

AbstractOnline action detection (OAD) is a practical yet challenging task, which has attracted increasing attention in recent years. A typical OAD system mainly consists of three modules: a frame-level feature extractor which is usually based on pre-trained deep Convolutional Neural Networks (CNNs), a temporal modeling module, and an action classifier. Among them, the temporal modeling module is crucial which aggregates discriminative information from historical and current features. Though many temporal modeling methods have been developed for OAD and other topics, their effects are lack of investigation on OAD fairly. This paper aims to provide an empirical study on temporal modeling for OAD including four meta types of temporal modeling methods, i.e. temporal pooling, temporal convolution, recurrent neural networks, and temporal attention, and uncover some good practices to produce a state-of-the-art OAD system. Many of them are explored in OAD for the first time, and extensively evaluated with various hyper parameters. Furthermore, based on our empirical study, we present several hybrid temporal modeling methods. Our best networks, i.e. , the hybridization of DCC, LSTM and M-NL, and the hybridization of DCC and M-NL, which outperform previously published results with sizable margins on THUMOS-14 dataset (48.6% vs. 47.2%) and TVSeries dataset (84.3% vs. 83.7%).

scholarly journals Joint Image Emotion Classification and Distribution Learning via Deep Convolutional Neural Network

2017 ◽  
Author(s):  
Jufeng Yang ◽  
Dongyu She ◽  
Ming Sun
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Prior Knowledge ◽  
State Of The Art ◽  
Ground Truth ◽  
Majority Voting ◽  
Emotion Classification ◽  
Voting Scheme ◽  
Label Distribution ◽  
Similarity Information

Visual sentiment analysis is attracting more and more attention with the increasing tendency to express emotions through visual contents. Recent algorithms in convolutional neural networks (CNNs) considerably advance the emotion classification, which aims to distinguish differences among emotional categories and assigns a single dominant label to each image. However, the task is inherently ambiguous since an image usually evokes multiple emotions and its annotation varies from person to person. In this work, we address the problem via label distribution learning (LDL) and develop a multi-task deep framework by jointly optimizing both classification and distribution prediction. While the proposed method prefers to the distribution dataset with annotations of different voters, the majority voting scheme is widely adopted as the ground truth in this area, and few dataset has provided multiple affective labels. Hence, we further exploit two weak forms of prior knowledge, which are expressed as similarity information between labels, to generate emotional distribution for each category. The experiments conducted on both distribution datasets, i.e., Emotion6, Flickr_LDL, Twitter_LDL, and the largest single emotion dataset, i.e., Flickr and Instagram, demonstrate the proposed method outperforms the state-of-the-art approaches.

Real-Time Streaming Data Analysis Using a Three-Way Classification Method for Sentimental Analysis

2020 ◽  
pp. 1377-1390
Author(s):  
Srinidhi Hiriyannaiah ◽  
G.M. Siddesh ◽  
K.G. Srinivasa
Keyword(s):  
Social Media ◽  
Opinion Mining ◽  
Streaming Data ◽  
Classification Method ◽  
Emotion Classification ◽  
Knowledge Based ◽  
Twitter Data ◽  
Internet Users ◽  
Word Classification ◽  
Three Stages

This article describes how recent advances in computing have led to an increase in the generation of data in fields such as social media, medical, power and others. With the rapid increase in internet users, social media has given power for sentiment analysis or opinion mining. It is a highly challenging task for storing, querying and analyzing such types of data. This article aims at providing a solution to store, query and analyze streaming data using Apache Kafka as the platform and twitter data as an example for analysis. A three-way classification method is proposed for sentimental analysis of twitter data that combines both the approaches for knowledge-based and machine-learning using three stages namely emotion classification, word classification and sentiment classification. The hybrid three-way classification approach was evaluated using a sample of five query strings on twitter and compared with existing emotion classifier, polarity classifier and Naïve Bayes classifier for sentimental analysis. The accuracy of the results of the proposed approach is superior when compared to existing approaches.

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