scholarly journals A Novel Framework using Zero Shot Learning Technique for Non-Factoid Question Answering System

2021 ◽  
Vol 16 (6) ◽  
pp. 0-0
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Question Answering ◽  
Neural Network Classifier ◽  
Multiple Sources ◽  
Question Answering System ◽  
Passage Level ◽  
Learning Technique ◽  
Natural Language Query ◽  
Main Components

Non-Factoid Question Answering (QA) is the next generation of textual QA systems, which gives passage level summaries for a natural language query, posted by the user. The main issue lies in the appropriateness of the generated summary. This paper proposes a framework for non-factoid QA system, which has three main components: (i) A deep neural network classifier, which produces sentence vector considering word correlation and context. (ii) Zero shot classifier that uses a multi-channel Convolutional Neural Network (CNN), to extract knowledge from multiple sources in the knowledge accumulator. This output acts as a knowledge enhancer that strengthens the passage level summary. (iii) Summary generator that uses Maximal Marginal Relevance (MMR) algorithm, which computes similarity among the query related answer and the sentences from zero shot classifier. This model is applied on the datasets WikiPassageQA and ANTIQUE. The experimental analysis shows that this model gives comparatively better results for WikiPassageQA dataset.

scholarly journals A Novel Framework Using Zero Shot Learning Technique for a Non-Factoid Question Answering System

2021 ◽  
Vol 16 (6) ◽  
pp. 1-13
Author(s):  
Akila Devi T. R. ◽  
K. Javubar Sathick ◽  
A. Abdul Azeez Khan ◽  
L. Arun Raj
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Question Answering ◽  
Neural Network Classifier ◽  
Multiple Sources ◽  
Question Answering System ◽  
Passage Level ◽  
Learning Technique ◽  
Natural Language Query ◽  
Main Components

Non-Factoid Question Answering (QA) is the next generation of textual QA systems, which gives passage level summaries for a natural language query, posted by the user. The main issue lies in the appropriateness of the generated summary. This paper proposes a framework for non-factoid QA system, which has three main components: (i) A deep neural network classifier, which produces sentence vector considering word correlation and context. (ii) Zero shot classifier that uses a multi-channel Convolutional Neural Network (CNN), to extract knowledge from multiple sources in the knowledge accumulator. This output acts as a knowledge enhancer that strengthens the passage level summary. (iii) Summary generator that uses Maximal Marginal Relevance (MMR) algorithm, which computes similarity among the query related answer and the sentences from zero shot classifier. This model is applied on the datasets WikiPassageQA and ANTIQUE. The experimental analysis shows that this model gives comparatively better results for WikiPassageQA dataset.

Withdrawn: Automated Diagnosis of Glaucoma Using Deep Neural Network Classifier

2019 ◽  
Vol 14 ◽  
Author(s):  
M. Madhumalini ◽  
T. Meera Devi
Keyword(s):  
Neural Network ◽  
Signal Transduction ◽  
Deep Neural Network ◽  
Neural Network Classifier ◽  
Current Signal ◽  
Automated Diagnosis ◽  
Signal Transduction Therapy ◽  
Copyright Permission ◽  
Legal Right

The article has been withdrawn on the request of the authors and the editor of the journal Current Signal Transduction Therapy. Bentham Science apologizes to the readers of the journal for any inconvenience this may have caused. BENTHAM SCIENCE DISCLAIMER: It is a condition of publication that manuscripts submitted to this journal have not been published and will not be simultaneously submitted or published elsewhere. Furthermore, any data, illustration, structure or table that has been published elsewhere must be reported, and copyright permission for reproduction must be obtained. Plagiarism is strictly forbidden, and by submitting the article for publication the authors agree that the publishers have the legal right to take appropriate action against the authors, if plagiarism or fabricated information is discovered. By submitting a manuscript the authors agree that the copyright of their article is transferred to the publishers, if and when the article is accepted for publication.

Bayesian Probability and Tanimoto Based Recurrent Neural Network for Question Answering System

2021 ◽  
Author(s):  
Veeraraghavan Jagannathan
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Question Answering ◽  
Bayesian Probability ◽  
Significant Information ◽  
Continuous Increase ◽  
Question Answering System ◽  
Relevant Result ◽  
Proper Nouns ◽  
Different Types

Question Answering (QA) has become one of the most significant information retrieval applications. Despite that, most of the question answering system focused to increase the user experience in finding the relevant result. Due to the continuous increase of web content, retrieving the relevant result faces a challenging issue in the Question Answering System (QAS). Thus, an effective Question Classification (QC), and retrieval approach named Bayesian probability and Tanimoto-based Recurrent Neural Network (RNN) are proposed in this research to differentiate the types of questions more efficiently. This research presented an analysis of different types of questions with respect to the grammatical structures. Various patterns are identified from the questions and the RNN classifier is used to classify the questions. The results obtained by the proposed Bayesian probability and Tanimoto-based RNN showed that the syntactic categories related to the domain-specific types of proper nouns, numeral numbers, and the common nouns enable the RNN classifier to reveal better result for different types of questions. However, the proposed approach obtained better performance in terms of precision, recall, and F-measure with the values of 90.14, 86.301, and 90.936 using dataset-2.

scholarly journals A Hybrid Neural Network BERT-Cap Based on Pre-Trained Language Model and Capsule Network for User Intent Classification

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Hai Liu ◽  
Yuanxia Liu ◽  
Leung-Pun Wong ◽  
Lap-Kei Lee ◽  
Tianyong Hao
Keyword(s):  
Neural Network ◽  
Neural Network Model ◽  
Question Answering ◽  
Semantic Information ◽  
Language Model ◽  
Dialogue System ◽  
Hybrid Neural Network ◽  
Question Answering System ◽  
User Intent ◽  
Vital Component

User intent classification is a vital component of a question-answering system or a task-based dialogue system. In order to understand the goals of users’ questions or discourses, the system categorizes user text into a set of pre-defined user intent categories. User questions or discourses are usually short in length and lack sufficient context; thus, it is difficult to extract deep semantic information from these types of text and the accuracy of user intent classification may be affected. To better identify user intents, this paper proposes a BERT-Cap hybrid neural network model with focal loss for user intent classification to capture user intents in dialogue. The model uses multiple transformer encoder blocks to encode user utterances and initializes encoder parameters with a pre-trained BERT. Then, it extracts essential features using a capsule network with dynamic routing after utterances encoding. Experiment results on four publicly available datasets show that our model BERT-Cap achieves a F1 score of 0.967 and an accuracy of 0.967, outperforming a number of baseline methods, indicating its effectiveness in user intent classification.

scholarly journals SINC: a scale-invariant deep-neural-network classifier for bulk and single-cell RNA-seq data

2019 ◽  
Vol 36 (6) ◽  
pp. 1779-1784 ◽  
Author(s):  
Chuanqi Wang ◽  
Jun Li
Keyword(s):  
Neural Network ◽  
Single Cell ◽  
Count Data ◽  
Deep Neural Network ◽  
Sequencing Depth ◽  
Supplementary Information ◽  
Neural Network Classifier ◽  
Rna Seq ◽  
Scale Invariant ◽  
Downstream Analysis

Abstract Motivation Scaling by sequencing depth is usually the first step of analysis of bulk or single-cell RNA-seq data, but estimating sequencing depth accurately can be difficult, especially for single-cell data, risking the validity of downstream analysis. It is thus of interest to eliminate the use of sequencing depth and analyze the original count data directly. Results We call an analysis method ‘scale-invariant’ (SI) if it gives the same result under different estimates of sequencing depth and hence can use the original count data without scaling. For the problem of classifying samples into pre-specified classes, such as normal versus cancerous, we develop a deep-neural-network based SI classifier named scale-invariant deep neural-network classifier (SINC). On nine bulk and single-cell datasets, the classification accuracy of SINC is better than or competitive to the best of eight other classifiers. SINC is easier to use and more reliable on data where proper sequencing depth is hard to determine. Availability and implementation This source code of SINC is available at https://www.nd.edu/∼jli9/SINC.zip. Supplementary information Supplementary data are available at Bioinformatics online.

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