Early Discovery of Emerging Entities in Microblogs♣
Satoshi Akasaki♣
Naoki Yoshinaga♣
Masashi Toyoda

akasaki@tkl.iis.u-tokyo.ac.jp

ynaga@iis.u-tokyo.ac.jp

toyoda@tkl.iis.u-tokyo.ac.jp♣ The University of Tokyo

Dataset: http://www.tkl.iis.u-tokyo.ac.jp/~akasaki/ijcai19/

Contribution• Introduce a novel task of discovering emerging entities (EEs) in microblogs• Revisit the definition of EEs so that it does not depend on temporal resource (KB etc.)• Propose a method that can discover EEs accurately, abundantly and quickly

Introduction

Task definition• Emerging entities (EEs) are appearing
ceaselessly in microblogs real-time

Find EEs as many and early as possible from microblog posts
Apple revealed new Mac Pro 2019 at WWDC2019 !!!
‘Pokémon Sword’ and ‘Shield’ are coming to the ...

Challenges:• Recognizing those EEs is important forapplications such as social listening

ü How to solidly define EEs without relying on temporal resources?
ü How to discover diverse EEs including long-tail ones?
ü How to discover EEs early while their frequencies are low?

Resource-independent definition of EEs
Define EEs only in terms of how people describe their contexts (emerging contexts (EC)):
ü Emerging Contexts: Contexts in which the writer assumed the reader does not know the existence of the entities
ü Emerging Entities: Entities in the state of being still observed in EC
Expected new voice actor “Sora Amamiya” appeared for the first time on live broadcasting! ...
Kyoto Animation’s TV anime “Tamako Market” started broadcasting in January 2013!
The name of the station to be built at the JR Nambu branch line is decided as Odaei Station!

By capturing those ECs that are independent of any resources, we can discover diverse EEs early sinceECs are common whether they are long-tail or not, and appear in the early-stage of their appearance(LSTM-)CRF based on timely distant supervisionCollect early-stage microblog posts where EEs are likely toappear by using distant supervision on time-series posts

2. Retrieve last N posts one year after the time ofcollecting ECs with collected EEs (Neg. ex.)

Experiments• Training data:

New trailer of SEKIRO released...

time1. Retrieve first N reposts where the titles of Wikipedia
articles appear as ECs with EEs (Pos. ex.)

1.0
0.8

LSTM-CRF[Lample+,16]

I finally cleared SEKIRO. Very hard.

SEKIROMac Pro 2019
Pokémon Sword

Mar. 21st, ‘17

SEKIRO is exciting game for me...

N posts

Jun. 14th, ‘17

Sep. 01th ‘17

1.0

1.0

Proposed (LSTM-CRF)

0.8

Proposed (LSTM-CRF) 0.8

Proposed (LSTM-CRF)

Proposed (CRF)

0.6

Proposed (CRF)

0.6

Proposed (CRF)

0.6
ü 222,092 posts collected using proposed methodfrom the period of 2012 to 2015 in our Twitter archive 0.4• Methods:

︙ 1 year after

WikipediaPrecision@kN reposts... announced new title “SEKIRO”

0.4

0.4

0.2

Baseline1

0.2

Baseline1

0.2

Baseline10.0

Baseline2

0.0

Baseline2

0.0

Baseline2

0100

200300ü Proposed: (LSTM-)CRF using the training dataü Baseline: Output NEs found by NER that are absent in a dictionary or
Wikipedia (Baseline1), or in the past tweets (Baseline2)• Evaluation:
ü Precision: Evaluate top-500 discovered entities from daily retweets,
which are sorted using confidence scores of each methodü (Relative) Recall: Evaluate how many target entities registered inWikipedia from Jan. ‘17 to Jun. ’18 could be discovered from all theretweets that include those entity surfaces
Examples of discovered EEs and ECs
Discovered EE Example contexts of entities (input tweet)
DRAGON BALL New fighting game DRAGON BALL FighterZ is announced!
FighterZ Trailer is become public. Expected to appear early 2018.
Kayakku LIVING Kayakku established WEB company by business acquisition,
which is related to housing named “Kayakku LIVING”

4005000100

200300400

Top k (# entities)

500

0100

200300400500

Discovered EEs from daily tweets
Day
Mar. 21stJun. 14th
Sep. 01st# Head(freq. > 100)
227
214
261

# Long-tail# Ambiguous(freq. < 100) (exist in Wikipedia)
110
84
106
77
110
66

Recall and lead-days over entity types of EEs
Type# Entities# Found (%)

PERSONARTWORKLOCATION
GROUP
OTHER
UNMAPPED
TOTAL

3851
4122
223
240
594891
13406

3238 (84.08%)
3703 (89.84%)
179 (80.27%)
152 (63.33%)
18 (30.51%)
3523 (72.03%)
10852 (80.95%)

Lead-days(mean/med.)
660 / 550
377 / 176
597 / 385
545 / 396
758 / 977
690 / 615
571 / 406