scholarly journals A Question Answering System on Holy Quran Translation Based on Question Expansion Technique and Neural Network Classification

2016 ◽  
Vol 12 (3) ◽  
pp. 169-177 ◽  
Author(s):  
Suhaib Kh. Hamed ◽  
Mohd Juzaiddin Ab Aziz
Keyword(s):  
Neural Network ◽  
Question Answering ◽  
Question Answering System ◽  
Neural Network Classification ◽  
Expansion Technique ◽  
Holy Quran

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 Chatbot Analytics Based on Question Answering System Movie Related Chatbot Case Analytics.pdf

2020 ◽  
Author(s):  
Jugal Shah ◽  
Sabah Mohammed
Keyword(s):  
Neural Network ◽  
Question Answering ◽  
Question Answering System

In this paper, an attempt has been made to understand<br>the importance of a neural network-based chatbot system for<br>movie-related queries.<br>

Intelligent pig raising knowledge question‐answering system based on neural network schemes

2021 ◽  
Author(s):  
Hsu‐Yang Kung ◽  
Ren‐Wu Yu ◽  
Chi‐Hua Chen ◽  
Chan‐Wei Tsai ◽  
Chia‐Yu Lin
Keyword(s):  
Neural Network ◽  
Question Answering ◽  
Question Answering System ◽  
Knowledge Question

scholarly journals Prediction of Answer Keywords using Char-RNN

2019 ◽  
Vol 9 (3) ◽  
pp. 2164
Author(s):  
Pratheek I ◽  
Joy Paulose
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Language Processing ◽  
Question Answering ◽  
Context Aware ◽  
Question Answering System ◽  
Novel Approach ◽  
Current Affairs ◽  
Generating Sequences ◽  
Gated Recurrent Unit

<p>Generating sequences of characters using a Recurrent Neural Network (RNN) is a tried and tested method for creating unique and context aware words, and is fundamental in Natural Language Processing tasks. These type of Neural Networks can also be used a question-answering system. The main drawback of most of these systems is that they work from a factoid database of information, and when queried about new and current information, the responses are usually bleak. In this paper, the author proposes a novel approach to finding answer keywords from a given body of news text or headline, based on the query that was asked, where the query would be of the nature of current affairs or recent news, with the use of Gated Recurrent Unit (GRU) variant of RNNs. Thus, this ensures that the answers provided are relevant to the content of query that was put forth.</p>

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.

Sign in / Sign up

Export Citation Format

Share Document