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        <title>SAB at SemEval-2023 Task 2: Does Linguistic Information Aid in Named Entity Recognition?</title>
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            <title>Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)</title>
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            <namePart type="given">Atul</namePart>
            <namePart type="given">Kr.</namePart>
            <namePart type="family">Ojha</namePart>
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    <abstract>This paper describes the submission to SemEval-2023 Task 2: Multilingual Complex Named Entity Recognition (MultiCoNER II) by team SAB. This task aims to encourage growth in the field of Named Entity Recognition (NER) by focusing on complex and difficult categories of entities, in 12 different language tracks. The task of NER has historically shown the best results when a model incorporates an external knowledge base or gazetteer, however, less research has been applied to examining the effects of incorporating linguistic information into the model. In this task, we explored combining NER, part-of-speech (POS), and dependency relation labels into a multi-task model and report on the findings. We determine that the addition of POS and dependency relation information in this manner does not improve results.</abstract>
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            <start>1131</start>
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%0 Conference Proceedings
%T SAB at SemEval-2023 Task 2: Does Linguistic Information Aid in Named Entity Recognition?
%A Biales, Siena
%Y Ojha, Atul Kr.
%Y Doğruöz, A. Seza
%Y Da San Martino, Giovanni
%Y Tayyar Madabushi, Harish
%Y Kumar, Ritesh
%Y Sartori, Elisa
%S Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F biales-2023-sab
%X This paper describes the submission to SemEval-2023 Task 2: Multilingual Complex Named Entity Recognition (MultiCoNER II) by team SAB. This task aims to encourage growth in the field of Named Entity Recognition (NER) by focusing on complex and difficult categories of entities, in 12 different language tracks. The task of NER has historically shown the best results when a model incorporates an external knowledge base or gazetteer, however, less research has been applied to examining the effects of incorporating linguistic information into the model. In this task, we explored combining NER, part-of-speech (POS), and dependency relation labels into a multi-task model and report on the findings. We determine that the addition of POS and dependency relation information in this manner does not improve results.
%R 10.18653/v1/2023.semeval-1.157
%U https://aclanthology.org/2023.semeval-1.157
%U https://doi.org/10.18653/v1/2023.semeval-1.157
%P 1131-1137

Markdown (Informal)

[SAB at SemEval-2023 Task 2: Does Linguistic Information Aid in Named Entity Recognition?](https://aclanthology.org/2023.semeval-1.157) (Biales, SemEval 2023)

• SAB at SemEval-2023 Task 2: Does Linguistic Information Aid in Named Entity Recognition? (Biales, SemEval 2023)

ACL

• Siena Biales. 2023. SAB at SemEval-2023 Task 2: Does Linguistic Information Aid in Named Entity Recognition?. In Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023), pages 1131–1137, Toronto, Canada. Association for Computational Linguistics.