garNER at SemEval-2023: Simplified Knowledge Augmentation for Multilingual Complex Named Entity Recognition

Proceedings of the The 17th International Workshop on Semantic Evaluation (SemEval-2023), pages 823–835 July 13-14, 2023 ©2023 Association for Computational Linguistics garNER at SemEval-2023: Simplified Knowledge Augmentation for Multilingual Complex Named Entity Recognition Md Zobaer Hossain♣†∗, Averie Ho Zoen So♣, Silviya Silwal♣, H. Andres Gonzalez Gongora♣, Ahnaf Mozib Samin♢, Jahedul Alam Junaed♠ Aritra Mazumder♠, Sourav Saha♠, Sabiha Tahsin Soha♠ ♣ University of Lorraine, Nancy, France, ♢ University of Malta, Malta ♠ Shahjalal University of Science and Technology, Sylhet, Bangladesh ♣{hossain6u, so2u, silwal1u, gonzal177u}@etu.univ-lorraine.fr ♢ahnaf.samin.22@um.edu.mt ♠{jahedul25, aritra73, sourav95, sabiha96}@student.sust.edu Abstract This paper presents our solution, garNER, to the SemEval-2023 MultiConer task. We pro- pose a knowledge augmentation approach by directly querying entities from the Wikipedia API and appending the summaries of the en- tities to the input sentence. These entities are either retrieved from the labeled training set (Gold Entity) or from off-the-shelf entity tag- gers (Entity Extractor). Ensemble methods are then applied across multiple models to get the final prediction. Our analysis shows that the added contexts are beneficial only when such contexts are relevant to the target-named en- tities, but detrimental when the contexts are irrelevant. 1 Introduction MultiCoNER is a shared task (Fetahu et al., 2023b) organized as part of the SemEval workshop to as- sess the performance of Named Entity Recognition (NER) systems across multiple languages. The task specifically addresses the recognition of complex named entities in a multilingual setting, such as product names, event names, and creative works. Complex named entities often have form varia- tions and may resemble normal language usage (eg. “Catch Me If You Can”), making them difficult to recognize with traditional NER techniques. To accurately classify these entities, domain-specific knowledge and advanced language modelling tech- niques are often considered necessary components of the systems submitted to this task. The introduction of Transformer-based models has resulted in significant advancements in vari- ous Natural Language Processing (NLP) tasks, in- cluding Named Entity Recognition (NER). The integration of pre-trained embeddings, trained on lengthy texts, has proven to be an effective tech- nique for modelling long-range dependencies and ∗† Project Lead. All authors have equal contributions resolving ambiguity in complex named entities within sentences (Peters et al., 2018; Akbik et al., 2018; Strakov´a et al., 2019). Among the systems submitted in MultiCoNER 2022, the winning system was DAMO-NLP (Wang et al., 2022), which ranked 1st in most languages (Malmasi et al., 2022b). The idea behind their approach is that incorporating a knowledge base system grants NER models a similar advantage as professional annotators, allowing them to re- trieve relevant documents and information from the knowledge base to disambiguate challenging samples in the dataset. The underlying principle of incorporating additional context into the original sentences is to enhance the contextual embeddings and thereby improve the contextual representations of the complex entities. In our revised method, we follow the same prin- ciple of extracting relevant information and em- ploying long-range dependencies to establish ro- bust token representations. However, a significant difference between our approach and that of the …

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