SeqL at SemEval-2022 Task 11: An Ensemble of Transformer Based Models for Complex Named Entity Recognition Task

Fadi Hassan; Wondimagegnhue Tufa; Guillem Collell; Piek Vossen; Lisa Beinborn; Adrian Flanagan; Kuan Eeik Tan

doi:10.18653/v1/2022.semeval-1.218

SeqL at SemEval-2022 Task 11: An Ensemble of Transformer Based Models for Complex Named Entity Recognition Task

Fadi Hassan
, Wondimagegnhue Tufa
, Guillem Collell
, Piek Vossen
, Lisa Beinborn
, Adrian Flanagan
, Kuan Eeik Tan

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review

Abstract

In this paper, we present a system for detecting complex named entities in multilingual and code-mix settings. We discuss the results obtained in task 11 (MultiCoNER) of the SemEval 2022 competition. The model is an ensemble of various transformer-based language models combined with a Conditional Random Field (CRF) layer. Our model ranks fourth in track 12 (multilingual track) and fifth in track 13 (code-mixed track). We describe the details of our model implementation and discuss the effect of different aggregation methods. Finally, we conduct additional analyses to understand the performance differences between languages.

Original language	English
Title of host publication	Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
Editors	Guy Emerson, Natalie Schluter, Gabriel Stanovsky, Ritesh Kumar, Alexis Palmer, Nathan Schneider, Siddharth Singh, Shyam Ratan
Publisher	Association for Computational Linguistics (ACL)
Pages	1583-1592
Number of pages	10
ISBN (Electronic)	9781955917803
DOIs	https://doi.org/10.18653/v1/2022.semeval-1.218
Publication status	Published - 2022
Event	16th International Workshop on Semantic Evaluation, SemEval 2022 - Seattle, United States Duration: 14 Jul 2022 → 15 Jul 2022

Conference

Conference	16th International Workshop on Semantic Evaluation, SemEval 2022
Country/Territory	United States
City	Seattle
Period	14/07/22 → 15/07/22

Bibliographical note

Funding Information:
The research by the authors of affiliation 1 and by Wondimagegnhue Tufa was funded by Huawei Finland.

Publisher Copyright:
© 2022 Association for Computational Linguistics.

Funding

The research by the authors of affiliation 1 and by Wondimagegnhue Tufa was funded by Huawei Finland.

Access to Document

10.18653/v1/2022.semeval-1.218

Persistent URL (handle)

Persistent link

Cite this

Hassan, F., Tufa, W., Collell, G., Vossen, P., Beinborn, L., Flanagan, A., & Tan, K. E. (2022). SeqL at SemEval-2022 Task 11: An Ensemble of Transformer Based Models for Complex Named Entity Recognition Task. In G. Emerson, N. Schluter, G. Stanovsky, R. Kumar, A. Palmer, N. Schneider, S. Singh, & S. Ratan (Eds.), Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) (pp. 1583-1592). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2022.semeval-1.218

Hassan, Fadi ; Tufa, Wondimagegnhue ; Collell, Guillem et al. / SeqL at SemEval-2022 Task 11 : An Ensemble of Transformer Based Models for Complex Named Entity Recognition Task. Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022). editor / Guy Emerson ; Natalie Schluter ; Gabriel Stanovsky ; Ritesh Kumar ; Alexis Palmer ; Nathan Schneider ; Siddharth Singh ; Shyam Ratan. Association for Computational Linguistics (ACL), 2022. pp. 1583-1592

@inproceedings{ea0a23598ee247b494fabe676291a40e,

title = "SeqL at SemEval-2022 Task 11: An Ensemble of Transformer Based Models for Complex Named Entity Recognition Task",

abstract = "In this paper, we present a system for detecting complex named entities in multilingual and code-mix settings. We discuss the results obtained in task 11 (MultiCoNER) of the SemEval 2022 competition. The model is an ensemble of various transformer-based language models combined with a Conditional Random Field (CRF) layer. Our model ranks fourth in track 12 (multilingual track) and fifth in track 13 (code-mixed track). We describe the details of our model implementation and discuss the effect of different aggregation methods. Finally, we conduct additional analyses to understand the performance differences between languages.",

author = "Fadi Hassan and Wondimagegnhue Tufa and Guillem Collell and Piek Vossen and Lisa Beinborn and Adrian Flanagan and Tan, \{Kuan Eeik\}",

note = "Funding Information: The research by the authors of affiliation 1 and by Wondimagegnhue Tufa was funded by Huawei Finland. Publisher Copyright: {\textcopyright} 2022 Association for Computational Linguistics.; 16th International Workshop on Semantic Evaluation, SemEval 2022 ; Conference date: 14-07-2022 Through 15-07-2022",

year = "2022",

doi = "10.18653/v1/2022.semeval-1.218",

language = "English",

pages = "1583--1592",

editor = "Guy Emerson and Natalie Schluter and Gabriel Stanovsky and Ritesh Kumar and Alexis Palmer and Nathan Schneider and Siddharth Singh and Shyam Ratan",

booktitle = "Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)",

publisher = "Association for Computational Linguistics (ACL)",

address = "United States",

}

Hassan, F, Tufa, W, Collell, G, Vossen, P, Beinborn, L, Flanagan, A & Tan, KE 2022, SeqL at SemEval-2022 Task 11: An Ensemble of Transformer Based Models for Complex Named Entity Recognition Task. in G Emerson, N Schluter, G Stanovsky, R Kumar, A Palmer, N Schneider, S Singh & S Ratan (eds), Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022). Association for Computational Linguistics (ACL), pp. 1583-1592, 16th International Workshop on Semantic Evaluation, SemEval 2022, Seattle, United States, 14/07/22. https://doi.org/10.18653/v1/2022.semeval-1.218

SeqL at SemEval-2022 Task 11: An Ensemble of Transformer Based Models for Complex Named Entity Recognition Task. / Hassan, Fadi; Tufa, Wondimagegnhue; Collell, Guillem et al.
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022). ed. / Guy Emerson; Natalie Schluter; Gabriel Stanovsky; Ritesh Kumar; Alexis Palmer; Nathan Schneider; Siddharth Singh; Shyam Ratan. Association for Computational Linguistics (ACL), 2022. p. 1583-1592.

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - SeqL at SemEval-2022 Task 11

T2 - 16th International Workshop on Semantic Evaluation, SemEval 2022

AU - Hassan, Fadi

AU - Tufa, Wondimagegnhue

AU - Collell, Guillem

AU - Vossen, Piek

AU - Beinborn, Lisa

AU - Flanagan, Adrian

AU - Tan, Kuan Eeik

PY - 2022

Y1 - 2022

N2 - In this paper, we present a system for detecting complex named entities in multilingual and code-mix settings. We discuss the results obtained in task 11 (MultiCoNER) of the SemEval 2022 competition. The model is an ensemble of various transformer-based language models combined with a Conditional Random Field (CRF) layer. Our model ranks fourth in track 12 (multilingual track) and fifth in track 13 (code-mixed track). We describe the details of our model implementation and discuss the effect of different aggregation methods. Finally, we conduct additional analyses to understand the performance differences between languages.

AB - In this paper, we present a system for detecting complex named entities in multilingual and code-mix settings. We discuss the results obtained in task 11 (MultiCoNER) of the SemEval 2022 competition. The model is an ensemble of various transformer-based language models combined with a Conditional Random Field (CRF) layer. Our model ranks fourth in track 12 (multilingual track) and fifth in track 13 (code-mixed track). We describe the details of our model implementation and discuss the effect of different aggregation methods. Finally, we conduct additional analyses to understand the performance differences between languages.

UR - https://www.scopus.com/pages/publications/85137544193

UR - https://www.scopus.com/pages/publications/85137544193#tab=citedBy

UR - https://aclanthology.org/2022.semeval-1.218/

U2 - 10.18653/v1/2022.semeval-1.218

DO - 10.18653/v1/2022.semeval-1.218

M3 - Conference contribution

AN - SCOPUS:85137544193

SP - 1583

EP - 1592

BT - Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)

A2 - Emerson, Guy

A2 - Schluter, Natalie

A2 - Stanovsky, Gabriel

A2 - Kumar, Ritesh

A2 - Palmer, Alexis

A2 - Schneider, Nathan

A2 - Singh, Siddharth

A2 - Ratan, Shyam

PB - Association for Computational Linguistics (ACL)

Y2 - 14 July 2022 through 15 July 2022

ER -

Hassan F, Tufa W, Collell G, Vossen P, Beinborn L, Flanagan A et al. SeqL at SemEval-2022 Task 11: An Ensemble of Transformer Based Models for Complex Named Entity Recognition Task. In Emerson G, Schluter N, Stanovsky G, Kumar R, Palmer A, Schneider N, Singh S, Ratan S, editors, Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022). Association for Computational Linguistics (ACL). 2022. p. 1583-1592 Epub 2022 Jul 14. doi: 10.18653/v1/2022.semeval-1.218

SeqL at SemEval-2022 Task 11: An Ensemble of Transformer Based Models for Complex Named Entity Recognition Task

Abstract

Conference

Bibliographical note

Funding

Access to Document

Persistent URL (handle)

Other files and links

Fingerprint

Cite this