Bianca Pereira

From Entity Recognition to

Entity Linking

07/05/2014

Based on the paper “From Entity Recognition to Entity Linking: a Survey of

Advanced Entity Linking Techniques” from Dai et al. 2012

Outline

• Motivation

• Overview of Entity Linking

• Instance-based Entity Linking Approach

• Experiments

• Conclusion

• Analysis of the Paper

• Relation with my PhD

THE BATTLE OF THE BOOGIE

Named Entity Recognition

k

Source: http://en.wikipedia.org/wiki/Mick_Jackson_(singer) (visited in 06/05/2014)

Named Entity Recognition

k

Source: http://en.wikipedia.org/wiki/Mick_Jackson_(singer) (visited in 06/05/2014)

Overview of Entity Linking

Databases

BiomedicalNatural Language

Processing

AI

Databases

BiomedicalNLP

AI

Entity Linking

Source: http://en.wikipedia.org/wiki/Mick_Jackson_(singer) (visited in 20/11/2013)

http://www.discogs.com/artist

/87624-Mick-Jackson

http://www.discogs.com/artist/6432

65-Elmar-Krohn?noanv=1

http://www.discogs.com/artist/49239

6-Dave-Jackson-2?noanv=1

http://www.discogs.com/artist/

148391-Sylvester-Levay

http://www.discogs.com/artist/16

9154-Jacksons-The

Tasks Inspired Entity Linking

Link-The-Wiki Track in INEX Web People Search Task in

SemEval

URL1

URL2

URLn

…

Person 1 Person 2

Person 3Person 4

Entity Linking Tasks

Entity Linking in TAC-KBP Gene Normalization in

BioCreative

http://www.discogs.com/artist

/87624-Mick-Jackson

NIL

Syntenin-1 ID:100754014

ID:6386mda-9

…

Problem Definition

Article-wide Salient Entity Linking Problem

Article-wide Entity Linking Problem

Instance-based Entity Linking Problem

Article-wide Salient EL Problem

Source: http://en.wikipedia.org/wiki/Michael_jordan (visited in 20/11/2013)

Article-wide EL Problem

Source: http://en.wikipedia.org/wiki/Blame_It_on_the_Boogie (visited in 06/05/2014)

Instance-based EL Problem

Source: http://en.wikipedia.org/wiki/Blame_It_on_the_Boogie (visited in 06/05/2014)

Instance-based Entity Linking Approach

Instance-based Entity Linking Approach

Challenges

1. Lack of suitable corpus for developing instance-based EL

systems.

2. Lack of context information for disambiguating each

individual instance.

The synthetic replicate of urocortin was found to bind with high

affinity to type 1 and type 2 CRF receptors and, based upon its

anatomic localization within the brain, was proposed to be a

natural ligand for the type 2 CRF receptors.

Classification

Local Classification

URL1 URL2 URL3 URL4

Classification

Local Classification Relational Classification

URL1 URL2 URL3 URL4

URL1 URL2 URL3 URL4

Classification

Local Classification Relational Classification

URL1 URL2 URL3 URL4

URL1 URL2 URL3 URL4

URL9

9

Collective Classification

URL1 URL2 URL3 URL4

URL3

5

URL4

7

URL9

9 URL1

5

URL2

0

URL5

URL1

3

Collective Entity Disambiguation

1. Discourse SalienceIn a given discourse there is precisely one entity that is the center of

attention.

2. TransitivityIf two mentions refer to the same entity, and one mention has been

linked to a database entry, the other should also be linked to the same entry.

Markov Logic Network Formulation

Observed FeaturesSaliency: Precede(x,y) ^ LinkTo(x,id) ^ Candidate (y,id) => LinkTo(y,id)

ID1 ID2 ID3 ID4

ID2

…Here, we demonstrate that rat syntetin-1, previously

published as syntenin-1 (syntenin), mda-9, or TACIP18 in

human, is a neurofascin-binding protein that exhibits a wide-

spread tissue expression pattern with a relative maximum in

brain. …

Markov Logic Network Formulation

Observed FeaturesSaliency: Precede(x,y) ^ LinkTo(x,id) ^ Candidate (y,id) => LinkTo(y,id)

Observed Features of the NeighborsTransitivity: Coreference(x,y) ^ LinkTo(x,idi) => LinkTo(y,idi)

ID1

…Here, we demonstrate that rat syntetin-1, previously

published as syntenin-1 (syntenin), mda-9, or TACIP18 in

human, is a neurofascin-binding protein that exhibits a wide-

spread tissue expression pattern with a relative maximum in

brain. …

Markov Logic Network Formulation

Observed FeaturesSaliency: Precede(x,y) ^ LinkTo(x,id) ^ Candidate (y,id) => LinkTo(y,id)

Observed Features of the NeighborsTransitivity: Coreference(x,y) ^ LinkTo(x,idi) => LinkTo(y,idi)

Unobserved Features of the NeighborsProtein-protein interaction: LinkTo(x,idi) ^ Candidate(y, idj) ^ PPIPartner(idi,

idj) => LinkTo(y, idj)

Syntanin-1 mda-9

ID1

ID2

ID9

Collective INFERENCE

URL1 URL2 URL3 URL4

URL3

5

URL4

7

URL9

9 URL1

5

URL2

0

URL5

URL1

3

Joint Inference

…Here, we demonstrate that rat syntetin-1, previously

published as syntenin-1 (syntenin), mda-9, or TACIP18 in

human, is a neurofascin-binding protein that exhibits a wide-

spread tissue expression pattern with a relative maximum in

brain. …

Transitivity: Coreference(x,y) ^ LinkTo(x,idi) => LinkTo(y,idi)

syntetin-1 syntetin-1

syntetin

mda-9

TACIP18

Joint Inference

New Constraints

Transitivity2: Coreference(x,y) ^ LinkTo(x,idi) ^ ¬exist idj.LinkTo(y, idj) =>

LinkTo(y, idi)

URL5

…Here, we demonstrate that rat syntetin-1, previously

published as syntenin-1 (syntenin), mda-9, or TACIP18 in

human, is a neurofascin-binding protein that exhibits a wide-

spread tissue expression pattern with a relative maximum in

brain. …

?

Joint Inference

New Constraints

Transitivity2: Coreference(x,y) ^ LinkTo(x,idi) ^ ¬exist idj.LinkTo(y, idj) =>

LinkTo(y, idi)

Coreference(x,y) => SuitablyLink(x) ^ SuitablyLink(y)

LinkTo(x,id) => SuitablyLink(x)

…Here, we demonstrate that rat syntetin-1, previously

published as syntenin-1 (syntenin), mda-9, or TACIP18 in

human, is a neurofascin-binding protein that exhibits a wide-

spread tissue expression pattern with a relative maximum in

brain. …

Experiments

Corpus

IGML Corpus (Instance-based Gene Mention Linking)

Training Set Test Set

Number of articles 282 262

Number of gene mentions 2,813 3,143

Number of linked Entrez Gene IDs 2,861 3,187

Number of words per article 215.86 228.91

Number of mentions per article 10.01 12.00

Number of words per mention 1.52 1.35

Number of IDs per mention 1.02 1.01

Corpus

IGML Corpus (Instance-based Gene Mention Linking)

Training Set Test Set

Number of articles 282 262

Number of gene mentions 2,813 3,143

Number of linked Entrez Gene IDs 2,861 3,187

Number of words per article 215.86 228.91

Number of mentions per article 10.01 12.00

Number of words per mention 1.52 1.35

Number of IDs per mention 1.02 1.01

Syntenin-1URL5

U

Corpus

IGML Corpus (Instance-based Gene Mention Linking)

Training Set Test Set

Number of articles 282 262

Number of gene mentions 2,813 3,143

Number of linked Entrez Gene IDs 2,861 3,187

Number of words per article 215.86 228.91

Number of mentions per article 10.01 12.00

Number of words per mention 1.52 1.35

Number of IDs per mention 1.02 1.01

Human and rat syntenin-1 The mammalian syntenin-1

Corpus – Gene Mention Recognition

Set Precision Recall F-Measure

Training 55.3 83.4 66.5

Test 66.2 82.7 65.1

Corpus– Training Set

0

10

20

30

40

50

60

70

80

90

Precision Recall Fmeasure

Optimal Linking

Best Linking

Worst Linking

Corpus – Test Set

0

10

20

30

40

50

60

70

80

90

Precision Recall F-Measure

Optimal Linking

Best Linking

Worst Linking

Evaluation

Training Test

Feature P R F P R F

Saliency Discourse 79.2 50.2 61.5 79.5 59.0 67.7

Protein-protein Interaction 79.4 51.1 62.2 80.1 59.8 68.5

Transitivity 78.5 49.5 60.7 78.6 58.8 67.2

Evaluation

Training Test

P R F P R F

Random Baseline 68.4 51.6 58.8 68.3 59.8 63.

8

Collective 79.1 52.0 62.8 78.4 61.0 68.

6

Collective + Filtering 79.3 52.0 62.9 78.8 61.0 68.

8

Individual 74.9 54.3 62.9 75.7 61.7 68.

0

Collective + Individual 74.5 55.7 63.7 74.9 64.8 69.

5

Collective + Individual + Filtering 79.9 54.9 65.1 77.8 65.3 71.

0

Conclusion

- Overview of Entity Linking

- Why is Instance-based Entity Linking more challenging?

- Suggestion of a solution to the problem

Analysis

Cons

- The results do not lead to any conclusion.

- Too much abbreviations in the paper.

- Does the approach converge to a optimal solution?

- How long does it take to give a solution?

- Is there any case that could not be disambiguated by

human annotators?

Pros

- Outlier

- “The instance-based EL task requires deeper linguistic

analysis and domain dependent knowledge to infer each

instance’s identity.”

Databases

BiomedicalNatural Language

Processing

AI

Semantic Web

How is it Related to my PhD?

I am working on the Entity Linking Topic.

Generic Approach Focus on Linguistic Features Linked Data as Knowledge Base Scalability

Thank you!