Collaborative Video Scene Annotation Based on Tag Cloud

Yamamoto, Daisuke; Masuda, T; Ohira, Susumu; Nagao, Katashi

doi:10.1007/978-3-540-89796-5_41

Cited by 13 publications

(6 citation statements)

References 12 publications

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“…The user-produced contents carry useful information about the articles' contents and users' attitudes (Yin et al, 2016). As the citation contexts revealed to be effective in scientific information retrieval (Eto, 2013), it seems rational to seek for improving the retrieval of scientific documents by the application of comments and recommendations, especially those posted by experts and peer reviews, because they are rich sources of information and appropriately represent the contents of the related articles (Rashidi et al, 2020;Koolen, 2014;Yin et al, 2016;Wang et al, 2012;Potthast et al, 2012;Cunningham and Nichols, 2008;Yamamoto et al, 2008;Yee et al, 2009;Hu et al, 2008). Given the differences of individuals in their knowledge horizons, any user may have her own perception of the document she reads and use different terms when commenting on it (Bouadjenek et al, 2016), and thereby bring about diversity in the wordings and terminologies.…”

Section: Discussionmentioning

confidence: 99%

Comment-enriched index terms improve the relevance and novelty of the ranking of the commented medical articles retrieved by an NLP system

Rashidi

Sotudeh

Nikseresht

2022

OIR

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PurposeThis study aimed to investigate how the enrichment of medical documents' index terms by their comments improves the relevance and novelty of the top-ranked results retrieved by an NLP system.Design/methodology/approachA semi-experimental pre-test and post-test research was designed to compare NLP-based indexes before and after being expanded by the comment terms. The experiments were conducted on a test collection of 13,957 documents commented by F1000-Prime reviewers. They were indexed at title, abstract, body and full-text levels. In total, 100 seed documents were randomly selected and served as queries. The textual similarity of the documents and queries was calculated using Lucene-more-like-this function and evaluated by the semantic similarity of their MeSH. The results novelty was measured using maximal marginal relevance and evaluated by their MeSH novelties. Normalized discounted cumulative gain was used to compare the basic and expanded indexes' precisions at 10, 20 and 50 top ranks.FindingsThe relevance and novelty of the results ranked at the top precision points was improved after expanding the indexes by the comment terms. The finding implies that meta-texts are effective in representing their mother documents, by adding dynamic elements to their rather static contents. It also provides further evidence about the merits of the application of social intelligence and collective wisdom reflected in the actions and reactions of users in tackling the challenges faced by NLP-based systems.Originality/valueThis is the first study to confirm that social comments on scientific papers improve the performance of information systems in terms of relevance and novelty.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-05-2022-0283.

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Section: Discussionmentioning

confidence: 99%

Comment-enriched index terms improve the relevance and novelty of the ranking of the commented medical articles retrieved by an NLP system

Rashidi

Sotudeh

Nikseresht

2022

OIR

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“…A video scene annotation method based on tag clouds has also been proposed [5]. Comments associated to a video and collected from YouTube are processed and a tag cloud is generated based on those comments.…”

Section: Related Workmentioning

confidence: 99%

YouTube Timed Metadata Enrichment Using a Collaborative Approach

Pinto

Viana

2018

Cryptology and Network Security

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Although the growth of video content in online platforms has been happening for some time, searching and browsing these assets is still very inefficient as rich contextual data that describes the content is still not available. Furthermore, any available descriptions are, usually, not linked to timed moments of content. In this paper, we present an approach for making social web videos available on YouTube more accessible, searchable and navigable. By using the concept of crowdsourcing to collect the metadata, our proposal can contribute to easily enhance content uploaded in the YouTube platform. Metadata, collected as a collaborative annotation game, is added to the content as time-based information in the form of descriptions and captions using the YouTube API. This contributes for enriching video content and enabling navigation through temporal links.

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“…Yamamoto, Masuda, Ohira, and Nagao () proposed a video scene annotation method based on tag clouds. Their proposed system, Synvie, enables users to click on a tag in a tag cloud (generated from user comments) while watching a video so that the clicked tag becomes associated with that particular time point of the video.…”

Section: Literature Reviewmentioning

confidence: 99%

Generic speech summarization of transcribed lecture videos: Using tags and their semantic relations

Kim

2014

Asso for Info Science & Tech

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We propose a tag-based framework that simulates human abstractors' ability to select significant sentences based on key concepts in a sentence as well as the semantic relations between key concepts to create generic summaries of transcribed lecture videos. The proposed extractive summarization method uses tags (viewer-and author-assigned terms) as key concepts. Our method employs Flickr tag clusters and WordNet synonyms to expand tags and detect the semantic relations between tags. This method helps select sentences that have a greater number of semantically related key concepts. To investigate the effectiveness and uniqueness of the proposed method, we compare it with an existing technique, latent semantic analysis (LSA), using intrinsic and extrinsic evaluations. The results of intrinsic evaluation show that the tag-based method is as or more effective than the LSA method. We also observe that in the extrinsic evaluation, the grand mean accuracy score of the tag-based method is higher than that of the LSA method, with a statistically significant difference. Elaborating on our results, we discuss the theoretical and practical implications of our findings for speech video summarization and retrieval.

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Collaborative Video Scene Annotation Based on Tag Cloud

Cited by 13 publications

References 12 publications

Comment-enriched index terms improve the relevance and novelty of the ranking of the commented medical articles retrieved by an NLP system

Comment-enriched index terms improve the relevance and novelty of the ranking of the commented medical articles retrieved by an NLP system

YouTube Timed Metadata Enrichment Using a Collaborative Approach

Generic speech summarization of transcribed lecture videos: Using tags and their semantic relations

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