32.3495, FYI: SemEval-2022 Task 11: Multilingual Complex Named Entity Recognition - Call for Participation

[The LINGUIST List]

LINGUIST List: Vol-32-3495. Thu Nov 04 2021. ISSN: 1069 - 4875.

Subject: 32.3495, FYI: SemEval-2022 Task 11: Multilingual Complex Named Entity Recognition - Call for Participation

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Date: Thu, 04 Nov 2021 19:49:01
From: Sudipta Kar [sudipkar at amazon.com]
Subject: SemEval-2022 Task 11: Multilingual Complex Named Entity Recognition - Call for Participation

 
Hi,
We invite you to participate in SemEval-2022 Task 11: Multilingual Complex
Named Entity Recognition (MultiCoNER).

Task Website: https://multiconer.github.io/
Codalab (Data download + Submission): 
https://competitions.codalab.org/competitions/36044

This task focuses on the detection of complex entities, such as movie, book,
music and product titles, in low context settings (short and uncased text).
The task covers 3 domains (sentences, search queries, and questions) and
provides data in 11 languages: English, Spanish, Dutch, Russian, Turkish,
Korean, Farsi, German, Chinese, Hindi, and Bangla. 
 
Here are some examples in English, Chinese, Bangla, Hindi, Russian, Korean,
and Farsi, where entities are enclosed inside brackets with their type:
* the original [ferrari daytona | PRODUCT] replica driven by [don johnson |
PERSON] in [miami vice | CreativeWork]
* 它 的 座 位 在 [圣 布 里 厄 | LOCATION] .
* স্টেশনটির মালিক [টাউনস্কেয়ার মিডিয়া | CORPORATION] ।
* यह [कनेल विभाग | LOCATION] की राजधानी है।
* в основе фильма — стихотворение [г. сапгира | PERSON] .
* [블루레이 디스크 | PRODUCT] : 광 기록 방식 저장매체의 하나
* [نینتندو | CORPORATION] / [باندای نامکو انترتینمنت | CORPORATION] – [برادران
سوپر ماریو نهایی | CreativeWork]

Additionally, a multilingual NER track is also offered for multilingual
systems that can process all languages. A code-mixed track allows participants
to build systems that process inputs with tokens coming from two languages.
For example, the following are some code-mixed examples from Turkish, Spanish,
Dutch, German, and English.
* it was produced at the [soyuzmultfilm | GROUP] studio in [moskova |
LOCATION] .
* [arturo vidal | PERSON] ( born 1987 ) , professional footballer playing for
[fútbol club barcelona | GROUP]
* daarmee promoveerde hij toen naar de [premier league | CORPORATION] .
* piracy has been a part of the [sultanat von sulu | LOCATION] culture .

The task focuses on detecting semantically ambiguous and complex entities in
short and low-context settings. Participants are welcome to build NER systems
for any number of languages. And we encourage to aim for a bigger challenge of
building NER systems for multiple languages. The task also aims at testing the
domain adaption capability of the systems by testing on additional test sets
on questions and short search queries.  

We have released training data for 11 languages along with a baseline system
to start with. Participants can submit their system for one language but are
encouraged to aim for a bigger challenge and build multi-lingual NER systems.

Task Website: https://multiconer.github.io/
Codalab Submission site:  https://competitions.codalab.org/competitions/36044
Mailing List: multiconer-semeval at googlegroups.com
Slack Workspace:
https://join.slack.com/t/multiconer/shared_invite/zt-vi3g97cx-MpqTvS07XX22S78n
RC2s0Q
Training Data: https://multiconer.github.io/dataset
Baseline System: https://multiconer.github.io/baseline

Shared task schedule:
Training data ready: September 3, 2021
Evaluation data ready: December 3, 2021
Evaluation start: January 10, 2022
Evaluation end: by January 31, 2022 (latest date; task organizers may choose
an earlier date)
System description paper submissions due: February 23, 2022
Notification to authors: March 31, 2022

Task organizers
Shervin Malmasi (Amazon)
Besnik Fetahu (Amazon)
Anjie Fang (Amazon) 
Sudipta Kar (Amazon) 
Oleg Rokhlenko (Amazon)

Please reach out to the organizers at
multiconer-semeval-organizers at googlegroups.com, or join the Slack workspace to
connect with the other participants and organizers.
 
Thank you,
Sudipta Kar
 



Linguistic Field(s): Computational Linguistics





 



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