D 2 KL ab at S em E val-2023 Task 2: Leveraging T - NER to Develop a Fine-Tuned Multilingual Model for Complex Named Entity Recognition (2023)

Abstract: This paper presents D2KLab’s system used for the shared task of “Multilingual Complex Named Entity Recognition (MultiCoNER II)”, as part of SemEval 2023 Task 2. The system relies on a fine-tuned transformer based language model for extracting named entities. In addition to the architecture of the system, we discuss our results and observations.

## introduction named entity recognition (ner) is a fundamental task in natural language processing (nlp) that aims to identify and classify named entities in text. while ner has been extensively studied in monolingual settings and with basic named entity types @xcite , the challenges of multilingual ner are significant due to variations in language structure, orthography, and morphology. the semeval-2023 task 2, "multilingual complex named entity recognition (multiconer ii)" @xcite , provides a platform for evaluating systems on this challenging task across 12 languages. the key challenges of this year's task include a fine-grained entity taxonomy with over 30 different classes and simulated errors added to the test set to make the task more realistic and difficult. in this paper, we present d2klab's system for multiconer ii, which relies on a fine-tuned transformer based language model. we also report our system's results and observations, along with a discussion of the challenges and opportunities for multilingual ner. our system's performance highlights the importance of fine-tuning on target languages and the effectiveness of transformer-based models for multilingual texts. ## related work in the field of named entity recognition (ner), researchers have made significant progress in re-cent years, particularly in addressing the challenges associated with recognizing complex entities and limited context situations. this work builds on the progress made in the multilingual ner task started in 2022 with the first edition of multiconer @xcite , where the key challenges included dealing with complex entities with limited context @xcite . @xcite outlined the challenges of ner in recognizing complex entities and in low-context situations. furthermore, @xcite extended this work to multilingual and code-mixed settings. this extension was also included in the multiconer dataset, and our work builds on this research. ## data the data collection methods used to compile the dataset used in multiconer is described in @xcite . the dataset comprises annotated sentences for 12 languages, namely bangla, german, english, spanish, farsi, french, hindi, italian, portuguese, swedish, ukrainian, and chinese, along a multilingual track. the data is in the conll-2002 format, which consists of one token per line with tab-separated columns indicating the word, part of speech, and named entity tag. each sentence is annotated with named entity labels issued from a tagset of 34 fine-grained labels. we also used a combination of publicly available datasets listed in ## methodology in this section, we describe the methodology used in our system. ## results our system generated a model that participated in all multiconer tracks, with macro-averaged f1 being the official ranking metric. ## conclusion in this paper, we presented the d2klab system which achieved a reasonable performance for the multilingual complex named entity recognition task, as part of semeval 2023 task 2. the use of a fine-tuned transformer-based language model for extracting named entities proved to be effective, with a macro f1-score of 0.605. however, our results also demonstrated that the system's performance varied across different languages. our implementation is available at https://github. com/d2klab/multiconer .