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Neural Processing Letters
Erschienen in: Neural Processing Letters 1/2023

30.06.2022

Augmenting Textbooks with cQA Question-Answers and Annotated YouTube Videos to Increase Its Relevance

verfasst von: Shobhan Kumar, Arun Chauhan

Erschienen in: Neural Processing Letters | Ausgabe 1/2023

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Abstract

The community question-answering (CQA) websites such as Quora1, and Reddit2, and YouTube provides a significant resource to the students. However, there is a redundancy issue which results in inadequate search results for a given question. On the other hand, e-book is primary source of knowledge for students. The Latent Dirichlet Allocation (LDA) topic model helps us to find the key topic of the e-book. The major flaw of this LDA is that it can’t capture the semantic knowledge in the documents. As a result, it fails to find the semantically cohesive, and meaningful topics. To address this issue, we propose a novel sBERT-LDA model, which augments the e-books with recommended question-answers and videos. We construct SiameseBERT (Bidirectional Encoder Representations from Transformers) network which provides the semantically relevant phrase embeddings. The model identifies the key topics of the e-book, after which sBERT is used to assess the similarity between the question-answers. This effort also provides advanced video indexing methods for each recommended video, allowing videos with “Table of Contents” and “Phrase Cloud” features to make videos more consumable. Experiments were carried out on question-answers datasets (Quora (QQP), TREC QA, and Yahoo Answers) as well as e-books on various subjects and across different grades.The model outperforms previous research by a large margin.
Vorheriger Artikel Dual Attention Mechanism Based Outline Loss for Image Stylization
Nächster Artikel Author Profiling in Code-Mixed WhatsApp Messages Using Stacked Convolution Networks and Contextualized Embedding Based Text Augmentation

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Metadaten
Titel
Augmenting Textbooks with cQA Question-Answers and Annotated YouTube Videos to Increase Its Relevance
verfasst von
Shobhan Kumar
Arun Chauhan
Publikationsdatum
30.06.2022
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 1/2023
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-022-10897-4

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