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Co-attentive multi-task convolutional neural network for facial expression recognition

Previous research on Facial Expression Recognition (FER) assisted by facial landmarks mainly focused on single-task learning or …
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Co-attentive multi-task convolutional neural network for facial expression recognition

Transformer-based Feature Reconstruction Network for Robust Multimodal Sentiment Analysis

Improving robustness against data missing has become one of the core challenges in Multimodal Sentiment Analysis (MSA), which aims to …
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Transformer-based Feature Reconstruction Network for Robust Multimodal Sentiment Analysis

TEXTOIR: An Integrated and Visualized Platform for Text Open Intent Recognition

TEXTOIR is the first integrated and visualized platform for text open intent recognition. It is composed of two main modules: open …
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TEXTOIR: An Integrated and Visualized Platform for Text Open Intent Recognition

Representation iterative fusion based on heterogeneous graph neural network for joint entity and relation extraction

Joint entity and relation extraction is an essential task in information extraction, which aims to extract all relational triples from …
赵康, 徐华, Yue Cheng, Xiaoteng Li, KaiGao
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Representation iterative fusion based on heterogeneous graph neural network for joint entity and relation extraction

Discovering New Intents with Deep Aligned Clustering

Discovering new intents is a crucial task in dialogue systems. Most existing methods are limited in transferring the prior knowledge …
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Discovering New Intents with Deep Aligned Clustering

Deep Open Intent Classification with Adaptive Decision Boundary

Open intent classification is a challenging task in dialogue systems. On the one hand, we should ensure the classification quality of …
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Deep Open Intent Classification with Adaptive Decision Boundary

Learning Modality-Specific Representations with Self-Supervised Multi-Task Learning for Multimodal Sentiment Analysis

Representation Learning is a significant and challenging task in multimodal learning. Effective modality representations should contain …
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Learning Modality-Specific Representations with Self-Supervised Multi-Task Learning for Multimodal Sentiment Analysis

CM-BERT: Cross-Modal BERT for Text-Audio Sentiment Analysis

Multimodal sentiment analysis is an emerging research field that aims to enable machines to recognize, interpret, and express emotion. …
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CM-BERT: Cross-Modal BERT for Text-Audio Sentiment Analysis

Objective Reduction in Many-Objective Optimization: Evolutionary Multiobjective Approaches and Comprehensive Analysis

Many-objective optimization problems bring great difficulties to the existing multiobjective evolutionary algorithms, in terms of …
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Objective Reduction in Many-Objective Optimization: Evolutionary Multiobjective Approaches and Comprehensive Analysis

Improving the Robustness of Machine Reading Comprehension Model with Hierarchical Knowledge and Auxiliary Unanswerability Prediction

Machine Reading Comprehension (MRC) aims to understand a passage and answer a series of related questions. With the development of deep …
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Improving the Robustness of Machine Reading Comprehension Model with Hierarchical Knowledge and Auxiliary Unanswerability Prediction

HGFM: A Hierarchical Grained and Feature Model for Acoustic Emotion Recognition

To solve the problem of poor classification performance of multiple complex emotions in acoustic modalities, we propose a hierarchical …
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HGFM: A Hierarchical Grained and Feature Model for Acoustic Emotion Recognition

FastBTM: Reducing the Sampling Time for Biterm Topic Model

Due to the popularity of social networks, such as microblogs and Twitter, a vast amount of short text data is created every day. Much …
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FastBTM: Reducing the Sampling Time for Biterm Topic Model

Deep Reinforcement Learning for Robust Emotional Classification in Facial Expression Recognition

For emotion classification in facial expression recognition (FER), the performance of both traditional statistical methods and …
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Deep Reinforcement Learning for Robust Emotional Classification in Facial Expression Recognition

Balancing Convergence and Diversity in Decomposition-Based Many-Objective Optimizers

The decomposition-based multiobjective evolutionary algorithms (MOEAs) generally make use of aggregation functions to decompose a …
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Balancing Convergence and Diversity in Decomposition-Based Many-Objective Optimizers

A New Dominance Relation Based Evolutionary Algorithm for Many-Objective Optimization

Many-objective optimization has posed a great challenge to the classical Pareto dominance-based multiobjective evolutionary algorithms …
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A New Dominance Relation Based Evolutionary Algorithm for Many-Objective Optimization

A joint model of extended LDA and IBTM over streaming Chinese short texts

With the prevalent of short texts, discovering the topics within them has become an important task. Biterm Topic Model (BTM) is more …
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A joint model of extended LDA and IBTM over streaming Chinese short texts

CH-SIMS: A Chinese Multimodal Sentiment Analysis Dataset with Fine-grained Annotation of Modality

Previous studies in multimodal sentiment analysis have used limited datasets, which only contain unified multimodal annotations. …
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CH-SIMS: A Chinese Multimodal Sentiment Analysis Dataset with Fine-grained Annotation of Modality

Heterogeneous graph neural networks for noisy few-shot relation classification

Relation classification is an essential and fundamental task in natural language processing. Distant supervised methods have achieved …
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Multi-Channel Convolutional Neural Networks with Adversarial Training for Few-Shot Relation Classification

The distant supervised (DS) method has improved the performance of relation classification (RC) by means of extending the dataset. …
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Multi-Channel Convolutional Neural Networks with Adversarial Training for Few-Shot Relation Classification

Discovering New Intents via Constrained Deep Adaptive Clustering with Cluster Refinement

Identifying new user intents is an essential task in the dialogue system. However, it is hard to get satisfying clustering results …
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Discovering New Intents via Constrained Deep Adaptive Clustering with Cluster Refinement

Deep Unknown Intent Detection with Margin Loss

Identifying the unknown (novel) user intents that have never appeared in the training set is a challenging task in the dialogue system. …
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Noisy Multiobjective Black-Box Optimization Using Bayesian Optimization

Expensive black-box problems are usually optimized by Bayesian Optimization (BO) since it can reduce evaluation costs via cheaper …
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Balancing Exploration and Exploitation in Multiobjective Batch Bayesian Optimization

Many applications such as hyper-parameter tunning in Machine Learning can be casted to multiobjective black-box problems and it is …
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A post-processing method for detecting unknown intent of dialogue system via pre-trained deep neural network classifier

With the maturity and popularity of dialogue systems, detecting user’s unknown intent in dialogue systems has become an important task. …
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A post-processing method for detecting unknown intent of dialogue system via pre-trained deep neural network classifier

Two-Stage Attention Network for Aspect-Level Sentiment Classification

Currently, most of attention-based works adopt single-stage attention processes during generating context representations toward …

Suggest what to tag: Recommending more precise hashtags based on users’ dynamic interests and streaming tweet content

Twitter is an online social networking microblogging service that allows registered users to broadcast 140-character messages called …
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Suggest what to tag: Recommending more precise hashtags based on users’ dynamic interests and streaming tweet content

User-IBTM: An Online Framework for Hashtag Suggestion in Twitter

Twitter, the global social networking microblogging service, allows registered users to post 140-character messages known as tweets. …

Finding overlapping community from social networks based on community forest model

Overlapping community detection is the key research work to discover and explore the social networks. A great deal of work has been …
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BitHash: An efficient bitwise Locality Sensitive Hashing method with applications

Locality Sensitive Hashing has been applied to detecting near-duplicate images, videos and web documents. In this paper we present a …
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Implicit feature identification in Chinese reviews using explicit topic mining model

The essential work of feature-specific opinion mining is centered on the product features. Previous related research work has often …
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Hierarchical emotion classification and emotion component analysis on chinese micro-blog posts

Text emotion analysis has long been a hot topic. With the development of social network, text emotion analysis on micro-blog posts …
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Emotion Cause Detection for Chinese Micro-Blogs Based on ECOCC Model

Micro-blog emotion mining and emotion cause extraction are essential in social network data mining. This paper presents a novel …

Chinese comments sentiment classification based on word2vec and SVMperf

Since the booming development of e-commerce in the last decade, the researchers have begun to pay more attention to extract the …
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An Experimental Investigation of Variation Operators in Reference-Point Based Many-Objective Optimization

Reference-point based multi-objective evolutionary algorithms (MOEAs) have shown promising performance in many-objective optimization. …
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A rule-based approach to emotion cause detection for Chinese micro-blogs

Emotion analysis and emotion cause extraction are key research tasks in natural language processing and public opinion mining. This …
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A rule-based approach to emotion cause detection for Chinese micro-blogs

A novel disjoint community detection algorithm for social networks based on backbone degree and expansion

Community detection in social networks is a key point to discover the functions and structure of social networks. A great deal of work …
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Text-based emotion classification using emotion cause extraction

In recent years, increasing impact of social networks on people’s opinions and decision making has attracted lots of attention. …
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Evolutionary Many-Objective Optimization Using Ensemble Fitness Ranking

In this paper, a new framework, called ensemble fitness ranking (EFR), is proposed for evolutionary many-objective optimization that …
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Constrained-hLDA for Topic Discovery in Chinese Microblogs

Since microblog service became information provider on web scale, research on microblog has begun to focus more on its content mining. …
Constrained-hLDA for Topic Discovery in Chinese Microblogs

Box office prediction based on microblog

As the importance and popularity of online social media has become more obvious, there are more researches aiming at making use of …
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An Improved NSGA-III Procedure for Evolutionary Many-Objective Optimization

Many-objective (four or more objectives) optimization problems pose a great challenge to the classical Pareto-dominance based …
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A Two-Level Hierarchical EDA Using Conjugate Priori

Estimation of distribution algorithms (EDAs) are stochastic optimization methods that guide the search by building and sampling …
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Implicit Feature Detection via a Constrained Topic Model and SVM

WeiWang, 徐华, Xiaoqiu Huang
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Implicit feature identification via hybrid association rule mining

In sentiment analysis, a finer-grained opinion mining method not only focuses on the view of the product itself, but also focuses on …
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Flexible job shop scheduling using hybrid differential evolution algorithms

This paper proposes hybrid differential evolution (HDE) algorithms for solving the flexible job shop scheduling problem (FJSP) with the …
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Effective search for genetic-based machine learning systems via estimation of distribution algorithms and embedded feature reduction techniques

Genetic-based machine learning (GBML) systems, which employ evolutionary algorithms (EAs) as search mechanisms, evolve rule-based …
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An integrated search heuristic for large-scale flexible job shop scheduling problems

The flexible job shop scheduling problem (FJSP) is a generalization of the classical job shop scheduling problem (JSP), where each …
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A Memetic Algorithm for the Multi-Objective Flexible Job Shop Scheduling Problem

In this paper, a new memetic algorithm (MA) is proposed for the muti-objective flexible job shop scheduling problem (MO-FJSP) with the …
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A hybrid harmony search algorithm for the flexible job shop scheduling problem

In this paper, a novel hybrid harmony search (HHS) algorithm based on the integrated approach, is proposed for solving the flexible job …
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Weakness Finder: Find product weakness from Chinese reviews by using aspects based sentiment analysis

Finding the weakness of the products from the customers’ feedback can help manufacturers improve their product quality and competitive …
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Effective search for Pittsburgh learning classifier systems via estimation of distribution algorithms

Pittsburgh-style learning classifier systems (LCSs), in which an entire candidate solution is represented as a set of variable number …
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Task scheduling using Bayesian optimization algorithm for heterogeneous computing environments

Abstract Efficient task scheduling, as a crucial step to achieve high performance for multiprocessor platforms, remains one of the …
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Questionnaires-based skin attribute prediction using Elman neural network

Skin attribute tests, especially for women, have become critical in the development of daily cosmetics in recent years. However, …
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Identifying Evaluative Sentences in Online Discussions

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Exploiting effective features for Chinese sentiment classification

Features play a fundamental role in sentiment classification. How to effectively select different types of features to improve …
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Exploiting effective features for Chinese sentiment classification

Constrained LDA for Grouping Product Features in Opinion Mining

In opinion mining of product reviews, one often wants to produce a summary of opinions based on product features. However, for the same …

Clustering Product Features for Opinion Mining

In sentiment analysis of product reviews, one important problem is to produce a summary of opinions based on product …
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An Elman neural network-based model for predicting anti-germ performances and ingredient levels with limited experimental data

Anti-germ performance test is critical in the production of detergents. However, actual biochemical tests are often costly and …
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An Elman neural network-based model for predicting anti-germ performances and ingredient levels with limited experimental data

A heuristic-based hybrid genetic-variable neighborhood search algorithm for task scheduling in heterogeneous multiprocessor system

Effective task scheduling, which is essential for achieving high performance in a heterogeneous multiprocessor system, remains a …
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Grouping Product Features Using Semi-Supervised Learning with Soft-Constraints

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Feature Subsumption for Sentiment Classification in Multiple Languages

An open problem in machine learning-based sentiment classification is how to extract complex features that outperform simple features; …

Effective Structure Learning for EDA via L1-Regularizedbayesian Networks

The Bayesian optimization algorithm (BOA) uses Bayesian networks to explore the dependencies between decision variables of an …
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An Empirical Study of Unsupervised Sentiment Classification of Chinese Reviews

This paper is an empirical study of unsupervised sentiment classification of Chinese reviews. The focus is on exploring the ways to …
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A Heuristic-Based Hybrid Genetic Algorithm for Heterogeneous Multiprocessor Scheduling

Effective task scheduling, which is essential for achieving high performance of parallel processing, remains challenging despite of …
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A Heuristic-Based Hybrid Genetic Algorithm for Heterogeneous Multiprocessor Scheduling

Cross Entropy and Adaptive Variance Scaling in Continuous EDA

This paper deals with the adaptive variance scaling issue incontinuous Estimation of Distribution Algorithms. A phenomenon is …
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