HyperTED

MARIELLA SABATINO GO!

insert a candidate

Master Degree course in

Cinema and Media Enginereeng

Fulvio Corno Raphaël Troncy

Josè Luis Redondo García

2

19842006

2014

.com

Mariella Sabatino - HyperTED, searching and browsing through fragments of TED Talks

3

Users

overwhelmed

with audio-visual

content

Which are the

relevant

fragments for the

user?

How can users easily find

related documents which

complement the video?

Can the video

be divided into

meaningful

fragments?

How can those

fragments be

properly

described?


Hyperlinking

Hyperlinking

Hyperlinking

4

Media Fragment

support

Media Fragment

annotation

Hyperlinking


5

how to recommend related media fragments within the same video collection

1 2 3

detecting segments

of interest within a

video

recommending

related media

fragments within the

same video collection

design a web application that

provides a rich

environment for

exploring a video

collection


Hyperlinking

6Mariella Sabatino - HyperTED, searching and browsing through fragments of TED Talks

2 31

Sections along the time

dimension of the media

resource with a start and an

end point.

A specific part of a

multimedia object.

http

://w

ww.w

3.o

rg/T

R/m

ed

ia-fra

gs/

http://www.w3.org/TR/media-frags/

7

TED Talks provide paragraphs:

a human-made subdivision of subtitles.


2 31


locating and classifying

elements of textual

documents into

pre-defined categories

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assigning a specific

linguistic meaning

between many

possibilities

9

“This is Nikita, a security guard from one of the bars

in St. Petersburg.”NER

PERSON

FUNCTION

LOCATION


2 31


2 31

St. Petersburg, Russia St. Petersburg, Florida

St. Petersburg, Pennsylvania St. Petersburg, Missouri (fictional)

NLP

Hyperlinking

Web Tools that expose

NER algorithms.


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Combines different NER tools available on Web

Unifies NER extractors in a common output

http

://n

erd

.eure

co

m.fr

/

http://nerd.eurecom.fr/

2 31


30 TED Talks fraction of retrieved

documents that are relevant

10

Selection of the meaningful

concepts on the subtitles;

Run of each extractor;

Comparison of the results.

fraction of relevant

documents that are retrieved

level of accuracy

considering both

13

AlchemyAPI 0,20 0,08 0,113

DataTXT 0,19 0,34 0,241

DBpedia Spotlight 0,14 0,33 0,192

OpenCalais 0,32 0,11 0,169

Saplo 0,00 0,00 -

TextRazor 0,17 0,36 0,228

THD 0,15 0,08 0,106

Yahoo! Content Analysis 0,31 0,05 0,086

Zemanta 0,41 0,10 0,155

Combined 0,12 0,45 0,195

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Relevance score

Disambiguation URL

Abstract

Topics

14

“I'm wearing a camera, just a simple webcam, a portable, battery-powered

projection system with a little mirror. These components communicate to my cell

phone in my pocket which acts as the communication and computation device.

And in the video here we see my student Pranav Mistry, who's really the genius

who's been implementing and designing this whole system...”


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15

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Clustering of consecutive Chapters with

similar annotations

Ordering based on relevance

Filtering the 1st quarter of relevance score

16


1 2 3

detecting segments


video

recommending

related media




provides a rich

environment for

exploring a video

collection



2 31

Open-source search engine

Document oriented

Comparable to a Relational DB

Query DSL based on JSON


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Topics and Entitiestiming references

UUID of the resource

Relevance

Meaning of the Talk

Chapter


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Topics

Courses about the same thematic

Entities

Too specific

Open Courseware

Open University

20

Interlinking throughchapters and

topic

Interlinking to openCourseware

and openUniversity


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21


1 2 3

detecting segments


video

recommending

related media




provides a rich

environment for

exploring a video

collection



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Detection of chapters working with TED APIs

Evaluation of some of the main NER Extractors

Hyperlinking between chapters and topics using elasticsearch

Building of a Responsive and Web compliant UI

LinkedUp Vici Competition top ten of the submissions.

Redondo García, Sabatino, Lisena and Troncy, Detecting Hot Spots in

Web Videos. In ISWC’14, Demo Track, Riva del Garda, Italy, 2014. http://ceur-ws.org/Vol-1272/paper_111.pdf

http://ceur-ws.org/Vol-1272/paper_111.pdf


Internet

HyperTED