Lotus at W ojood NER Shared Task: Multilingual Transformers: Unveiling Flat and Nested Entity Recognition (2023)

Abstract: We introduce our systems developed for two subtasks in the shared task “Wojood” on Arabic NER detection, part of ArabicNLP 2023. For Subtask 1, we employ the XLM-R model to predict Flat NER labels for given tokens using a single classifier capable of categorizing all labels. For Subtask 2, we use the XLM-R encoder by building 21 individual classifiers. Each classifier corresponds to a specific label and is designed to determine the presence of its respective label. In terms of performance, our systems achieved competitive micro-F1 scores of 0.83 for Subtask 1 and 0.76 for Subtask 2, according to the leaderboard scores.

## introduction named entity recognition (ner) is crucial for natural language processing (nlp), enabling the extraction of entities like names and locations from texts. given the rich linguistic diversity and varied dialects of arabic, ner becomes especially challenging @xcite . arabic, spoken by 420 million natives, is one of the top ten global languages @xcite @xcite . its lack of capital letters amplifies its morphological complexity, contrasting ner ease in english due to its varied dialects and rich history. arabic can be broadly classified into three distinct forms @xcite : classical arabic (ca), modern standard arabic (msa), and dialectal arabic (da) @xcite . although ca is the esteemed language of most religious arabic texts, msa, recognized as an official language by the united nations, finds its presence in contemporary media, formal correspondences, and the academic sphere. da, on the contrary, dominates informal day-to-day communications @xcite . the increasing volume of arabic content on digital platforms, driven by the proliferation of social media, has led to a surge in the demand for arabic ner. beyond its general applications, ner serves specialized domains, enabling tasks such as relation extraction @xcite , entity linking @xcite , event extraction @xcite , coreference resolution @xcite , and machine translation @xcite , historically, most arabic ner research has focused on direct, flat entity recognition techniques. however, the introduction of the wojood corpus @xcite ) marks a pivotal shift. this corpus, which forms the foundation of the arabicnlp 2023 wojood @xcite shared task, stands out for its extensive reach, encompassing more than 550k tokens from msa and its respective dialects. all of these are carefully annotated across a spectrum of 21 different entity types. the shared task @xcite highlights two principal ner challenges: two significant challenges arise in this context. first, despite progress in nlp, nested ner @xcite @xcite remains relatively uncharted compared to its flat ner counterpart, which has been deeply explored through cutting-edge linguistic, statistical, and neural techniques @xcite @xcite . second, there exists a conspicuous lack of detailed and expansive datasets designed specifically for nested ner. consequently, addressing the demands of subtask 2 poses a more significant challenge compared to subtask 1. our paper offers the following contributions: figure 2: statistics about tokens distribution in train and development sets. the organization of this paper is as follows. in section 2, we present prior and recent research on arabic ner. section 3 presents a comprehensive analysis of the dataset. section 4 describes our proposed system, experimental setup, and results. in section 5, we conclude and point out ideas for future research. ## related work arabic ner has seen notable advancements with various corpora, such as ontonotes 5, which features 18 entity types from msa @xcite , and others such as anercorp, caner-corpus, and the expansive aqmar corpus @xcite @xcite . the wojood corpus stands out, supporting named entities over 21 types and spanning both msa and dialects @xcite . although ner methods have transitioned from rule-based to deep learning, the integration of lstm-crf @xcite and pre-trained embeddings has been transformative. the introduction of bert highlighted the potential of transformers in ner @xcite . nested ner challenges persist, but innovations such as multilayer bilstm and pyramid architectures signal progress @xcite @xcite . ## data in this section, we provide a detailed description of the dataset released by the wojoodner organizers, which comprises arabic tokens, where less than half of the dataset consists of named entities. the wojood corpus encompasses a comprehensive and diverse array of flat and nested named entities, representing a new split distinct from the established wojood paper @xcite . ## conclusion and future work in this paper, we detail our xlm-r based systems for two subtasks in the arabicnlp 2023 wojood ner shared task. subtask 1 utilized a single classifier, while subtask 2 developed 21 labelspecific classifiers. our models achieved micro-f1 scores of 0.83 and 0.76 for subtasks 1 and 2, respectively, according to official leaderboard scores. we also compared our systems with state-of-the-art arabert and marbert models. in future work, we plan to incorporate data augmentation methods, including sentence mixing and back-translation. additionally, we would adopt a meta-ensembling approach, integrating models such as arabert, marbert, xlm-r, and ar-bert, to enhance performance on the unique and diverse wojood corpus.