Semantic Tags for Lecture Videos

Imran, Ali Shariq; Rahadianti, Laksmita; Cheikh, Faouzi Alaya; Yayilgan, Sule Yildirim

doi:10.1109/icsc.2012.36

Cited by 16 publications

(7 citation statements)

References 9 publications

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“…Imran and Cheikh (2012) first proposed the multimedia learning object framework for video lectures that opened up the niche for utilizing both implicit and explicit metadata obtained from textual content and non-textual cues for content organizing, structuring, and classification of video learning objects. A number of classification approaches as a result with respect to lecture videos have evolved over the years, most of which make use of natural language processing (NLP) either directly on the accompanying audio transcript as in (Imran et al, 2012b), or extracted automatically from the lecture images employing OCR as in (Imran et al, 2012a) or via speech-to-text such as in (Dessì et al, 2018).…”

Section: Related Workmentioning

confidence: 99%

Integrating word embeddings and document topics with deep learning in a video classification framework

Kastrati

Imran

Kurti

2019

Pattern Recognition Letters

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The advent of MOOC platforms brought an abundance of video educational content that made the selection of best fitting content for a specific topic a lengthy process. To tackle this challenge in this paper we report our research e↵orts of using deep learning techniques for managing and classifying educational content for various search and retrieval applications in order to provide a more personalized learning experience. In this regard, we propose a framework which takes advantages of feature representations and deep learning for classifying video lectures in a MOOC setting to aid e↵ective search and retrieval. The framework consists of three main modules. The first module called pre-processing concerns with video-to-text conversion. The second module is transcript representation which represents text in lecture transcripts into vector space by exploiting di↵erent representation techniques including bag-of-words, embeddings, transfer learning, and topic modeling. The final module covers classifiers whose aim is to label video lectures into the appropriate categories. Two deep learning models, namely feed-forward deep neural network (DNN) and convolutional neural network (CNN) are examined as part of the classifier module. Multiple simulations are carried out on a large-scale real dataset using various feature representations and classification techniques to test and validate the proposed framework.

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Section: Related Workmentioning

confidence: 99%

Integrating word embeddings and document topics with deep learning in a video classification framework

Kastrati

Imran

Kurti

2019

Pattern Recognition Letters

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“…Results showed that the accuracy between the text and the tags, computed by the semantic distance, was correlated 6 with the results in multiple choice questions tests. A similar approach with Wordnet was carried out by Imran et al [16] in order to tag lecture videos from the transcripts. In this case semantic distance was computed to perform sense disambiguation process.…”

Section: Concept Maps In Visual Learningmentioning

confidence: 99%

Building concept maps by adapting semantic distance metrics to Wikipedia

Fuentes-Lorenzo

Morato

Sánchez-Cuadrado

et al. 2019

EFI

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Wide Web -is not organized in a specific way and natural language processing techniques are usually needed in order to obtain acceptable results. In this research Wikipedia is proposed since it does not have such limitations. This article defines an approach to adapt path-based semantic similarity measures to Wikipedia for building concept maps. Experimental evaluation with a well-known set of human similarity judgments shows that the Wikipedia adapted metrics obtains equal or even better results when compared with the non-adapted approaches.

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“…"Implicit" is automatic meta-data generation: meta-data that is not created deliberately by users, but either generated and assigned as static meta-data, or generated on the fly, by software, applying intelligent analysis to objects of all types (text, images, audio, video, etc.). In case of our MLO Framework, we developed such a tool to analyze a text and assign "semantic tags" also known as "keywords" to LO based on the content and context of this text, even if the words do not appear in the text at all [14]. It would also discard texts referring to something completely different.…”

Section: A Media Analysis and Processing Unit (Mapu)mentioning

confidence: 99%

Multimedia learning objects framework for e-learning

Imran

Cheikh

2012

2012 International Conference on E-Learning and E-Technologies in Education (ICEEE)

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With the growing interest in flexible and blended education, the usage of recorded lecture videos as a part of learning material, is becoming an everyday activity in most of the higher education institutions. Flexible education provides students with the opportunity to select how, when, and where to study compared to ordinary on-campus education. Additionally, lecture videos contain most of the instructional content. The content of these videos are in general non-scripted and unedited, and thus do not provide the required level of interactivity. Therefore, these videos fail to captivate students' attention for a long time, and thus their effective use remains a challenge. In this regard, Multimedia Learning Object (MLO) can play an important role in future flexible education. MLOs are media rich documents that have pedagogical values encapsulated with all needed features such as navigation structures and surrogates. We propose in this paper a framework for interactive MLOs in distance and blended education. The framework supports creation, storage, distribution, and evaluation of automatically extracted learning objects from digital media. The framework not only conforms to industry standards, but also provide novel methods and techniques to develop Video Learning Objects (VLOs).

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Semantic Tags for Lecture Videos

Cited by 16 publications

References 9 publications

Integrating word embeddings and document topics with deep learning in a video classification framework

Integrating word embeddings and document topics with deep learning in a video classification framework

Building concept maps by adapting semantic distance metrics to Wikipedia

Multimedia learning objects framework for e-learning

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