A Novel Knowledge Base Question Answering Model Based on Knowledge Representation and Recurrent Convolutional Neural Network

Liu, Changhao; He, Ting; Xiong, Yingjie; Wang, Huazhen; Chen, Jian

doi:10.1109/icss50103.2020.00031

Cited by 2 publications

(2 citation statements)

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“… ----- ------Opinion-aware " Answer " Generation " for " Review-driven Question " Answering " in E-Commerce " [2] ------------Knowledge " Question-Answering " System " Based on " Knowledge " Graph " of " Traditional " Chinese Medicine " [3]  ------------Technical " Q8A " Site " Answer " Recommendation via " Question Boosting " [4] ------ ------An " improved " human-in-the-loop " model " for finegrained " object " recognition " with " batch-based question " answering [5]  - ---------A Novel " Knowledge " Base Question " Answering Model " Based " on " Knowledge " Representation and " Recurrent " Convolutional " Neural Network " [6] ------------Dynamic " Updating " of the Knowledge " Base for a Large-Scale " Question Answering " System " [7]  --------- -…”

Section: Ref Papers Title Objectives a Cp Ct C Eft E Irp Qrcc R Sc S A "mentioning

confidence: 99%

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Deep Learning Based Question Answering System (Survey)

Saeed,

ashraf,

Jhanjhi

2023

Preprint

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Deep learning-based question answering systems have transformed the discipline of natural language processing (NLP) by automating the extraction of answers from textual data. This survey paper provides a captivating overview of these systems, exploring methodologies, techniques, and architectures such as recurrent neural networks (RNNs), BERT model, and transformer models. Extractive and generative approaches are examined, alongside the challenges of handling complex questions, managing noisy input, and addressing rare or unseen words. This survey serves as a stimulating reference, offering valuable insights to researchers and practitioners, fueling innovation and advancement in question answering systems within NLP.

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Section: Ref Papers Title Objectives a Cp Ct C Eft E Irp Qrcc R Sc S A "mentioning

confidence: 99%

“…To create a KB-QA framework, the method described in [6] combines "knowledge representation and recurrent neural network (RNN)". The model is composed of three sections: the production of potential replies, the mining of object connections, and the acquisition of knowledge's representation from a knowledge base.…”

Section: Detailed Literaturementioning

confidence: 99%