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Enriching media fragments with named entities for video classification

Enriching media fragments with named entities for video classification
Enriching media fragments with named entities for video classification
With the steady increase of videos published on media sharing platforms such as Dailymotion and YouTube, more and more efforts are spent to automatically annotate and organize these videos. In this paper, we propose a framework for classifying video items using both textual features such as named entities extracted from subtitles, and temporal features such as the duration of the media fragments where particular entities are spotted. We implement four automatic machine learning algorithms for multiclass classification problems, namely Logistic Regression (LG), K-Nearest Neighbour (KNN), Naive Bayes (NB) and Support Vector Machine (SVM). We study the temporal distribution patterns of named entities extracted from 805 Dailymotion videos. The results show that the best performance using the entity distribution is obtained with KNN (overall accuracy of 46.58%) while the best performance using the temporal distribution of named entities for each type is obtained with SVM (overall accuracy of 43.60%). We conclude that this approach is promising for automatically classifying online videos.
Li, Yunjia
3a0d988e-b5e3-43c9-a268-dc14b5313547
Rizzo, Giuseppe
f19ab8da-0fbc-41df-97eb-bc8caa58893c
Garcia, Jose Luis Redondo
f4771184-5569-4459-a80c-61a9906327fd
Troncy, Raphael
c8dad007-f619-4533-9b0a-0b278a9c9828
Wald, Mike
90577cfd-35ae-4e4a-9422-5acffecd89d5
Wills, Gary
3a594558-6921-4e82-8098-38cd8d4e8aa0
Li, Yunjia
3a0d988e-b5e3-43c9-a268-dc14b5313547
Rizzo, Giuseppe
f19ab8da-0fbc-41df-97eb-bc8caa58893c
Garcia, Jose Luis Redondo
f4771184-5569-4459-a80c-61a9906327fd
Troncy, Raphael
c8dad007-f619-4533-9b0a-0b278a9c9828
Wald, Mike
90577cfd-35ae-4e4a-9422-5acffecd89d5
Wills, Gary
3a594558-6921-4e82-8098-38cd8d4e8aa0

Li, Yunjia, Rizzo, Giuseppe, Garcia, Jose Luis Redondo, Troncy, Raphael, Wald, Mike and Wills, Gary (2013) Enriching media fragments with named entities for video classification. At First Worldwide Web Workshop on Linked Media (LiME-2013) First Worldwide Web Workshop on Linked Media (LiME-2013), Brazil. 13 - 17 May 2013.

Record type: Conference or Workshop Item (Paper)

Abstract

With the steady increase of videos published on media sharing platforms such as Dailymotion and YouTube, more and more efforts are spent to automatically annotate and organize these videos. In this paper, we propose a framework for classifying video items using both textual features such as named entities extracted from subtitles, and temporal features such as the duration of the media fragments where particular entities are spotted. We implement four automatic machine learning algorithms for multiclass classification problems, namely Logistic Regression (LG), K-Nearest Neighbour (KNN), Naive Bayes (NB) and Support Vector Machine (SVM). We study the temporal distribution patterns of named entities extracted from 805 Dailymotion videos. The results show that the best performance using the entity distribution is obtained with KNN (overall accuracy of 46.58%) while the best performance using the temporal distribution of named entities for each type is obtained with SVM (overall accuracy of 43.60%). We conclude that this approach is promising for automatically classifying online videos.

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More information

Published date: 13 May 2013
Venue - Dates: First Worldwide Web Workshop on Linked Media (LiME-2013), Brazil, 2013-05-13 - 2013-05-17
Organisations: Web & Internet Science

Identifiers

Local EPrints ID: 352219
URI: https://eprints.soton.ac.uk/id/eprint/352219
PURE UUID: 20623f62-fb23-4a4a-ba14-f951a319b7a8
ORCID for Gary Wills: ORCID iD orcid.org/0000-0001-5771-4088

Catalogue record

Date deposited: 08 May 2013 13:49
Last modified: 06 Jun 2018 13:03

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