LMN at S em E val-2022 Task 11: A Transformer-based System for E nglish Named Entity Recognition (2022)

Abstract: Processing complex and ambiguous named entities is a challenging research problem, but it has not received sufficient attention from the natural language processing community. In this short paper, we present our participation in the English track of SemEval-2022 Task 11: Multilingual Complex Named Entity Recognition. Inspired by the recent advances in pretrained Transformer language models, we propose a simple yet effective Transformer-based baseline for the task. Despite its simplicity, our proposed approach shows competitive results in the leaderboard as we ranked 12 over 30 teams. Our system achieved a macro F1 score of 72.50% on the held-out test set. We have also explored a data augmentation approach using entity linking. While the approach does not improve the final performance, we also discuss it in this paper.

## introduction recognizing complex named entities (nes) is a challenging research problem, but it has not received sufficient attention from the natural language processing community @xcite . complex nes can be complex noun phrases (e.g., national baseball hall of fame and museum), gerunds (e.g., saving private ryan), infinitives (e.g., to build a fire), or even full clauses (e.g., i capture the castle). this ambiguity makes it difficult to recognize them based on their context @xcite @xcite . in this paper, we describe our participation in the english track of semeval-2022 task 11: multilingual complex named entity recognition @xcite . inspired by the recent success of transformer-based pre-trained language models in many nlp tasks @xcite @xcite @xcite @xcite @xcite , we propose a simple but effective transformer-based baseline for the task. despite its simplicity, our proposed approach shows promising results: the official ranking indicated that our system achieved a macro f 1 score of 72.50% on the test set and ranked 12th out of 30 teams. we have also explored a data augmentation approach using entity linking. while the approach does not improve the final performance, we also discuss it in this paper. in the following sections, we first describe the related work in section 2 and the proposed method in section 3. we then describe the experiments and their results in section 4. finally, section 5 concludes this work and discusses potential future research directions. ## related work many previous named entity recognition (ner) methods are based on the sequence labeling approach @xcite @xcite @xcite @xcite @xcite . for example, @xcite introduced a neural architecture that uses convolutional neural networks (cnns) to encode tokens combined with a crf layer for the classification. many other studies used recurrent neural networks (rnns) instead of cnns to encode the input and a crf for the prediction @xcite . with the recent rise of pre-trained language models, recent ner models typically make use of contextdependent embeddings such as elmo @xcite or bert @xcite . while neural-based models have achieved impressive results on popular benchmark datasets like conll03 and ontonotes @xcite @xcite , these models typically do not perform well on complex/unseen entities @xcite . complex named entities (e.g., titles tag description {b,i}-per a named entity of a person {b,i}-loc a named entity of a location {b,i}-grp a named entity of a group {b,i}-corp a named entity of a corporation {b,i}-prod a named entity of a product {b,i}-cw a named entity of a creative work o not a named entity ## conclusion in future work, we plan to conduct a thorough error analysis and apply visualization techniques to understand our models better @xcite . in addition, as pretrained transformer models are typically computationally expensive and have many parameters, we are also interested in reducing the computational complexity of our base-line models using compression techniques @xcite @xcite .