Recent progress in leveraging deep learning methods for question answering

2022 ◽  
Author(s):  
Tianyong Hao ◽  
Xinxin Li ◽  
Yulan He ◽  
Fu Lee Wang ◽  
Yingying Qu
Keyword(s):  
Deep Learning ◽  
Recent Progress ◽  
Question Answering ◽  
Learning Methods

Code Mixed Question Answering Challenge using Deep Learning Methods

2020 ◽  
Author(s):  
Thara S. ◽  
Sampath E. ◽  
Venkata Sitarami Reddy B. ◽  
Vidhya Sai Bhagavan M. ◽  
Phanindra Reddy M.
Keyword(s):  
Deep Learning ◽  
Question Answering ◽  
Learning Methods

scholarly journals A Review on Recent Progress in Machine Learning and Deep Learning Methods for Cancer Classification on Gene Expression Data

Processes ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 1466
Author(s):  
Aina Umairah Mazlan ◽  
Noor Azida Sahabudin ◽  
Muhammad Akmal Remli ◽  
Nor Syahidatul Nadiah Ismail ◽  
Mohd Saberi Mohamad ◽  
...  
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Deep Learning ◽  
Gene Expression Data ◽  
Recent Progress ◽  
Cancer Classification ◽  
Expression Data ◽  
Classification Methods ◽  
Healthcare Applications ◽  
Learning Methods

Data-driven model with predictive ability are important to be used in medical and healthcare. However, the most challenging task in predictive modeling is to construct a prediction model, which can be addressed using machine learning (ML) methods. The methods are used to learn and trained the model using a gene expression dataset without being programmed explicitly. Due to the vast amount of gene expression data, this task becomes complex and time consuming. This paper provides a recent review on recent progress in ML and deep learning (DL) for cancer classification, which has received increasing attention in bioinformatics and computational biology. The development of cancer classification methods based on ML and DL is mostly focused on this review. Although many methods have been applied to the cancer classification problem, recent progress shows that most of the successful techniques are those based on supervised and DL methods. In addition, the sources of the healthcare dataset are also described. The development of many machine learning methods for insight analysis in cancer classification has brought a lot of improvement in healthcare. Currently, it seems that there is highly demanded further development of efficient classification methods to address the expansion of healthcare applications.

scholarly journals A Model of Convolutional Neural Network Combined with External Knowledge to Measure the Question Similarity for Community Question Answering Systems

2021 ◽  
Vol 11 (3) ◽  
pp. 194-201
Author(s):  
Van-Tu Nguyen ◽  
◽  
Anh-Cuong Le ◽  
Ha-Nam Nguyen
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Question Answering ◽  
Learning Models ◽  
Learning Methods ◽  
Data Set ◽  
External Knowledge ◽  
Community Question Answering ◽  
Proposed Model

Automatically determining similar questions and ranking the obtained questions according to their similarities to each input question is a very important task to any community Question Answering system (cQA). Various methods have applied for this task including conventional machine learning methods with feature extraction and some recent studies using deep learning methods. This paper addresses the problem of how to combine advantages of different methods into one unified model. Moreover, deep learning models are usually only effective for large data, while training data sets in cQA problems are often small, so the idea of integrating external knowledge into deep learning models for this cQA problem becomes more important. To this objective, we propose a neural network-based model which combines a Convolutional Neural Network (CNN) with features from other methods so that the deep learning model is enhanced with addtional knowledge sources. In our proposed model, the CNN component will learn the representation of two given questions, then combined additional features through a Multilayer Perceptron (MLP) to measure similarity between the two questions. We tested our proposed model on the SemEval 2016 task-3 data set and obtain better results in comparison with previous studies on the same task.

Data Science in Economics: Comprehensive Review of Advanced Machine Learning and Deep Learning Methods

2020 ◽  
Author(s):  
Saeed Nosratabadi ◽  
Amir Mosavi ◽  
Puhong Duan ◽  
Pedram Ghamisi ◽  
Filip Ferdinand ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Data Science ◽  
Learning Methods ◽  
Comprehensive Review

Sequence-Based Deep Learning Frameworks on Enhancer-Promoter Interactions Prediction

2020 ◽  
Vol 26 ◽  
Author(s):  
Xiaoping Min ◽  
Fengqing Lu ◽  
Chunyan Li
Keyword(s):  
Gene Expression ◽  
Deep Learning ◽  
Dna Sequences ◽  
Experimental Methods ◽  
Learning Methods ◽  
Comprehensive Review ◽  
Genomic Features ◽  
Disease Mechanisms ◽  
Evaluation Strategies ◽  
Learning Frameworks

: Enhancer-promoter interactions (EPIs) in the human genome are of great significance to transcriptional regulation which tightly controls gene expression. Identification of EPIs can help us better deciphering gene regulation and understanding disease mechanisms. However, experimental methods to identify EPIs are constrained by the fund, time and manpower while computational methods using DNA sequences and genomic features are viable alternatives. Deep learning methods have shown promising prospects in classification and efforts that have been utilized to identify EPIs. In this survey, we specifically focus on sequence-based deep learning methods and conduct a comprehensive review of the literatures of them. We first briefly introduce existing sequence-based frameworks on EPIs prediction and their technique details. After that, we elaborate on the dataset, pre-processing means and evaluation strategies. Finally, we discuss the challenges these methods are confronted with and suggest several future opportunities.

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