S amsung Research C hina - B eijing at S em E val-2023 Task 2: An AL - R Model for Multilingual Complex Named Entity Recognition (2023)

Abstract: This paper describes our system for SemEval-2023 Task 2 Multilingual Complex Named EntityRecognition (MultiCoNER II). Our teamSamsung Research China - Beijing proposesan AL-R (Adjustable Loss RoBERTa) model toboost the performance of recognizing short andcomplex entities with the challenges of longtaildata distribution, out of knowledge base andnoise scenarios. We first employ an adjustabledice loss optimization objective to overcomethe issue of long-tail data distribution, which isalso proved to be noise-robusted, especially incombatting the issue of fine-grained label confusing. Besides, we develop our own knowledgeenhancement tool to provide related contextsfor the short context setting and addressthe issue of out of knowledge base. Experimentshave verified the validation of our approaches.

## introduction named entity recognition is an important task in natural language processing. in low-context data @xcite , semantic ambiguity, complex entities, multilingual and code-mixed settings make this task even more difficult. recognizing complex entities in low-context situations was recently outlined by @xcite . multilingual and code-mixed settings were extended through @xcite . to address these issues, semeval-2022 task 11 multilingual complex named entity recognition @xcite received 34 system papers, the best of which were able to accurately recognise complex entities by incorporating external knowledge related to the data @xcite . however, when the entity recognition scenario is more complex, the top system @xcite has drawbacks that make it difficult to cope. semeval-2023 task 2 multiconer ii multilingual complex named entity recognition @xcite aims to build a system to address the following problems: 1). the entities are complex and long-tailed illustrated by figure 1 . 2). the entities are out of knowledge-base entities. 3). the noisy scenarios like the presence of spelling mistakes and typos. in this paper, we build an al-r (adjustable loss roberta ) based system for multiconer ii task. our system utilizes the roberta-large @xcite as the pre-trained model and fine tunes it on the english dataset @xcite . for the long-tailed dataset and noise scene, we add the adjustable dice loss @xcite to overcome the issue of long tail and help the system more robust. because the dataset is low-context, we build a knowledge retrieval module @xcite via wikipedia dump foot_0 , from which we can perform sentence knowledge retrieval and entity knowledge retrieval for the dataset. in order to make our system more generalized, we integrate multiple al-r models and let them jointly determine the entity label. extensive experiments and ablation studies on the english track show that our system is much more superior to the baseline system, and the overall macro-f1 in the official test set is 0.8309 foot_1 . ## related work named entity recognition (ner) is a basic natural language understanding task in information extraction (ie) @xcite , which can be extensively used in many fields like information retrieval @xcite ), question answering @xcite , dialogues @xcite and so on. pre-training language model (plm) in recent years has achieved remarkable advance in ner tasks. however, it still faces some difficulty in the situation of short context and complex entities @xcite , which has been formulated by semeval-2022 task 11 @xcite . to address this issue, one classic approach is to use span-based or entity-aware plm to gain enhanced token representations, like ernie @xcite , spanbert @xcite and luke @xcite . another classic approach is grouped by the different decoding methods, including pointer based @xcite , token pairs based @xcite and span based @xcite . external knowledge @xcite @xcite is also an effective approach to achieve top performance in the field of short text and complex entities. but when facing the situation of out of knowledge base, noisy scenarios, and long-tail data distribution, only by knowledge is vulnerable to address the new issue. in this paper, we still adopt external knowledge to build the backbone of our system, but to address the out of knowledge base problem, we build our own knowledgeenhancement tool. @xcite proposed selfadjusting dice loss for data-imbalanced nlp tasks and achieved significant performance boost. we inherit this idea in our system to overcome the issue of long-tail distribution in multiconer ii datasets. ## data we train and test our models on the english track via multiconer2-data @xcite . english dataset foot_2 of multiconer2-data contains a training set of size 16778, a dev set size of 800, and a test set of size 249980. it consists of 6 coarsegrained labels and 33 fine-grained labels. ## methodology we introduce a knowledge-enhancement and adjustable-loss named entity recognition system to overcome the main challenges foot_3 paid much attention in this big event. first, to alleviate the insufficiency of the context of the input texts, which is critical for distinguishing and recognizing entities especially in the field of short texts, we feed the inputs into our wikipedia-based tool to get the knowledge-enhancement context. second, to combat the performance degradation caused by the long-tail data distribution, we select and deliberately design the dice loss, here called adjustableloss, to be the optimization object. meanwhile, we introduce the label-smoothing technology to decrease the overfitting of some grained labels belonging to the same coarse labels. the architecture of our scheme is shown in figure 2 . ## conclusion in this paper, we build a system for complex finegrained ner task. our system assembles multiple al-r models containing roberta based pre-trained model and adjustable dice loss to overcome the issues of long-tailed dataset and noise entities. we construct the knowledge retrieval module from which sentence knowledge augmentation and entity knowledge augmentation can be performed on low-context data. in multiconer ii task, our system ranks 2nd on english track. although our model is somewhat robust to noisy scenarios, in the future we will investigate better ways to deal with noisy entities.