concentric semantic snapshot

THE CONCENTRIC NATURE OF NEWS SEMANTIC SNAPSHOTS

JOSÉ LUIS REDONDO GARCIAGIUSEPPE RIZZORAPHAËL TRONCY

@peputo / [email protected]

@giusepperizzo / [email protected]

@rtroncy / [email protected]

mailto:[email protected]




2

Overview

May 1, 2023 8th International Conference on Knowledge Capture

1. Introducing the Problem: Contextualizing News Items o The News Semantic Snapshot (NSS)

2. Previous Work:o Frequency-based Functionso Multidimensional Relevancy Approach

3. A Concentric Model for Generating NSS

3

Overview



2. Previous Work:o Frequency-based Functionso Multidimensional Relevancy Approach


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The Problem: Contextualizing News


Wolfgang Schäuble

Finance Minister Ruling Party in Ger.

Christian Democratic Union

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5May 1, 2023 8th International Conference on Knowledge Capture

Sarah Harrison

WikiLeaks Editor Airport in Moscow

Sheremetyevo

The Problem: Contextualizing News1 2 3

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Contextualizing News: Applications


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7

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News Semantic Snapshot (NSS) [1]


News Semantic Snapshot (NSS)

[1] Redondo et al., Generating the Semantic Snapshot of Newscasts using Entity Expansion, ICWE 2015, Rotterdam.

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Recreating the NSS

News Semantic Snapshot8th International Conference on Knowledge Capture

ea eb ec ed ef eg eh ei ej ek el em

ea ec eh ej ek em

(2) SELECTION: filtering, clustering, ranking…

(1) EXPANSION: query generation, search, document retrieval…

ea eb ec ed

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May 1, 2023 9

Involving: (experts in the news domain + users)Dimensions:

Play with the data and help us to extend it at: https://github.com/jluisred/NewsConceptExpansion/wiki/Golden-Standard-Creation

News Semantic Snapshot: Gold Standard

(1) Video Subtitles(2) Image in the video(3) Text in the video image(4) Suggestions of an expert(5) Related articles

8th International Conference on Knowledge Capture

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https://github.com/jluisred/NewsConceptExpansion/wiki/Golden-Standard-Creation


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Recreating the NSS



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ea eb ec ed

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May 1, 2023

(1) Bringing in Missing Entities: News Entity Expansion

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1.a)


Web sites to be crawled:- Google- L1 : A set of 10

internationals English speaking newspapers

- L2 : A set of 3 international newspapers used in GS

Temporal Window:- 1W: - 2W: Annotation filtering- Schema.org

1.b)Parameters [1]:

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May 1, 2023 12



ea ec eh ej ek em



ea eb ec ed

Recall (E. Expansion) = 0.91

Recall (NER on Subtitles) = 0.42

Recreating the NSS1 2 3


(NSS)

(Entity Expansion)

0

N

FIdeal(ei)

(NSS)

FX(ei)

=?MNDCG

The Selection Problem: 1 2 3

14

Overview



2. Previous Work:o Frequency-based Functiono Multidimensional Relevancy Approach


May 1, 2023 15

1º Entity Frequency SNOW Workshop 2014 [2]


A

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[2] Redondo et al., Describing and Contextualizing Events in TV News Show}, SNOW Workshop, WWW 2014, Seoul, Korea.

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Frequency Based: Results


(NSS)

(Expansion)

FREQ0

N

(NSS

)

F(Laura Poitras) = 2

F(Glenn Greenwald) = 1

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May 1, 2023 15th International Conference on Web Engineering (ICWE) 17

(Fr) (FrGaussian)

Multidimensional ApproachICWE 2015 [1]

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May 1, 2023

POPULARITY (FPOP) EXPERT RULES (FEXP)

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- Based on Google Trends- w = 2 months- μ + 2*σ (2.5%)

Example:- [ Location, = 0.48 ]- [ Person, = 0.74 ]- [ Organization, = 0.95 ]- [ < 2 , = 0.0 ]

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Multidimensional Approach1 2 3

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- News Entity Expansion + Dimensions Generate the News Semantic Snapshot

- Best score: 0.667 in MNDCG at 10, better than BS1/2

• Collection: CSE (Google + 2W + Schema.org)• Ranking:

• Expert Rules• Popularity


Multidimensionality: Results1 2 3


(NSS))

(Expansion)

FREQ POP EXP

+ + =

(NSS

)

Multidimensionality: Results1 2 3

May 1, 2023 8th International Conference on Knowledge Capture 21

Follow up: Fine-Tuning

1. Exploit Google Relevance (+1.80%)2. Promote Subtitle Entities (+2.50%)3. Exploit Named Entity Extractor’s confidence (+0.20%)4. Interpret popularity Dimension (+1.40%)5. Performing Clustering before Filtering (-0.60%)

- NO SIGNIFICANT IMPROVEMENT -

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(NSS)

TuneFunction XFREQ POP EXP

No Improvement: Why?Re-ShuffleOriginal

(NSS

)

How many Dimensions?How to combine them?

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23

Overview



2. Previous Work:o Frequency-based Functiono Multidimensional Relevancy Approach



Thinking Outside the Box:

1. Is there room for improvement?2. Is MNDCG a good measure to

evaluate NSS? 3. How to significantly improve the

approach?

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Room for Improvement?

GAIN

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Room for Improvement?1 2 3


How to Evaluate NSS? MNDCG:• Too focused on success at first positions (decay

Function)

• NSS intends to be flexible, ranking is application-dependent

COMPACTNESS:• Prioritizes coverage over ranking• Compromise between: Recall and NSS size• Recall*: positives are weighted according to score in GT

(NSS)

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Compactness:Recall: 22/33 = 0.66

Sa = 27

Sb = 33

Sc = 54

Sa = 27

Sb = 33

Sc= 54

(NSS

)

A B CA

B

C

> >

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Re-thinking the Approach: Concentric Snapshot

Duality in News Entity Spectrum:• REPRESENTATIVE entities:

• Driving the plot of the story, sometimes evident for users. • RELEVANT entities

• Related to former via specific reasons

Exploit the entity semantic relations

Popular?

Suggested by Expert?

Informative?Unexpected?

Interesting?

Explicative?Highly

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Hypothesis: Concentric SnapshotCORE:• Representative entities

• Spottable via Frequency dimensions

• High degree of cohesiveness

CRUST:• Attached to the Core via

particular relations

• Agnostic to relevancy nature: informativeness, interestingness, etc.

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Core Generationa) Representative entities: Frequency Dimension

(NSS)

b) Cohesiveness (DBpedia)

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Crust Generation

The number of Web documents talking simultaneously about a particular entity e and the Core:

??

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Experimental Settings

1. Entity Frequency• Core1: Jaro-Winkler > 0.9 • Core2: Frequency based on Exact String matching

2. Cohesiveness: • Everything is Connected Engine [3]• Skb(e1, e2) > 0.125

CORE: (2 configurations)

[3] Everything is Connected Engine:

https://github.com/mmlab/eice

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1. Candidates for CRUST generation: • Ex1: 1° ICWE2015 by R*(50): L2+Google, F3 1W, Gauss+ POP• Ex2: 2° ICWE 2015 by R*(50): L2+Google, F3 1W, Freq + POP

2. Function for attaching entities to CORE:• SWEB(ei, Core) over Google CSE, default Configuration

CRUST:

Experimental Settings1 2 3

(2 configurations)


• Core+Crust: • CrustOnly:

Projecting CORE and CRUST:

(NSS)

(Expansion)

CORE CRUST Core+Crust CrustOnly


(2 configurations)


Baselines:

BAS01: best run in ICWE 2015 at R*(50)BAS02: second best run in ICWE 2015 at R*(50)

FREQPOPEXP



Results: Compactness

Percentage decrease of 36.9% over BAS01

IdealGT: size of SSN according to Gold Standard

(2*2*2 + 2) Runs

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Results: Recall* over N1 2 3


Conclusion• News applications can benefit from the News Semantic Snapshot (NSS)

• Proposed a concentric based model for generating the NSS:• Formalizes duality in entities (Representative VS Relevant)• Exploit the entity semantic relations between Core and Crust.• Accommodate into a single model different relevancy dimensions via the notion of

web presence ( SWeb )

• Concentric model better reproduces the NSS:• Better Compactness: 36.9% over BAS01• Similar recall, Smaller size

• Concentric model easier to implement:

• Core can be reproduced via Frequency Dimension• Crust brings up relevant entities without having to deal with fuzzy dimensions

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Future• Extend the number of videos considered in GT:

From 5 to 23 (+18), check [4] for more information

• Spot not only relationships between Crust and the Core but also predicates that characterize them:

[4] https://github.com/jluisred/NewsConceptExpansion/wiki/Golden-Standard-Creation

Editor in WikiLeaks

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JOSÉ LUIS REDONDO GARCIAGIUSEPPE RIZZORAPHAËL TRONCY

@peputo / [email protected]

@giusepperizzo / [email protected]

@rtroncy / [email protected]

http://www.slideshare.net/joseluisredondo/concentric-semantic-snapshot

Visit poster at booth:

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