SemEval-2022 Task 11: Multilingual Complex Named Entity Recognition (MultiCoNER)

Identifying named entities is, in general, a practical and challenging task in the field of Natural Language Processing. Named Entity Recognition on the code-mixed text is further challenging due to the linguistic complexity resulting from the nature of the mixing. This paper addresses the submission of team CM-NEROne to the SEMEVAL 2022 shared task 11 MultiCoNER. The Code-mixed NER task aimed to identify named entities on the codemixed dataset. Our work consists of Named Entity Recognition (NER) on the code-mixed dataset by leveraging the multilingual data. We achieved a weighted average F1 score of 0.7044, i.e., 6% greater than the baseline.

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“…MultiCoNER baseline (Malmasi et al, 2022b) The XLM-RoBERTa base with CRF model is used as a baseline for NER.…”

Section: Baselinesmentioning

confidence: 99%

CMNEROne at SemEval-2022 Task 11: Code-Mixed Named Entity Recognition by leveraging multilingual data

Dowlagar¹,

Mamidi²

2022

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

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“…SemEval 2022 task 11 (Malmasi et al, 2022b) containing a total of 13 sub-tasks is a complex NER task which focuses on detecting semantically ambiguous and complex entities in short and lowcontext settings (Meng et al, 2021). For the purpose of testing the domain adaption capability of the participating models, the task not only set 11 base sub-tasks: English, Spanish, Dutch, Russian, Turkish, Korean, Farsi, German, Chinese, Hindi and Bangla, but also set two additional testing sets on questions and short queries: Multilingual, and code-mixed (Fetahu et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

CMB AI Lab at SemEval-2022 Task 11: A Two-Stage Approach for Complex Named Entity Recognition via Span Boundary Detection and Span Classification

Pu¹,

Liu²,

Yang³

et al. 2022

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

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This paper presents a solution for the SemEval-2022 Task 11 Multilingual Complex Named Entity Recognition. What is challenging in this task is detecting semantically ambiguous and complex entities in short and low-context settings. Our team (CMB AI Lab) propose a two-stage method to recognize the named entities: first, a model based on biaffine layer is built to predict span boundaries, and then a span classification model based on pooling layer is built to predict semantic tags of the spans. The basic pre-trained models we choose are XLM-RoBERTa and mT5. The evaluation result of our approach achieves an F1 score of 84.62 on sub-task 13, which ranks the third on the learder board.

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“…The SemEval-2022 Task 11, Multilingual Complex Named Entity Recognition (MultiCoNER) (Malmasi et al, 2022b) aims at developing complex NER systems for 11 languages. The task focuses on detecting semantically ambiguous and complex entities in short, lowercased, low-context monolingual, and multilingual settings.…”

Section: Introductionmentioning

confidence: 99%

L3i at SemEval-2022 Task 11: Straightforward Additional Context for Multilingual Named Entity Recognition

Boroş¹,

González-Gallardo²,

Moreno³

et al. 2022

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

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This paper summarizes the participation of the L3i laboratory of the University of La Rochelle in the SemEval-2022 Task 11, Multilingual Complex Named Entity Recognition (MultiCoNER). The task focuses on detecting semantically ambiguous and complex entities in short and low-context monolingual and multilingual settings. We argue that using a language-specific and a multilingual language model could improve the performance of multilingual and mixed NER. Also, we consider that using additional contexts from the training set could improve the performance of a NER on short texts. Thus, we propose a straightforward technique for generating additional contexts with and without the presence of entities. Our findings suggest that, in our internal experimental setup, this approach is promising. However, we ranked above average for the highresource languages and lower than average for low-resource and multilingual models.

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SemEval-2022 Task 11: Multilingual Complex Named Entity Recognition (MultiCoNER)

Cited by 50 publications

References 7 publications

CMNEROne at SemEval-2022 Task 11: Code-Mixed Named Entity Recognition by leveraging multilingual data

CMNEROne at SemEval-2022 Task 11: Code-Mixed Named Entity Recognition by leveraging multilingual data

CMB AI Lab at SemEval-2022 Task 11: A Two-Stage Approach for Complex Named Entity Recognition via Span Boundary Detection and Span Classification

L3i at SemEval-2022 Task 11: Straightforward Additional Context for Multilingual Named Entity Recognition

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