Delving Deep into Regularity: A Simple but Effective Method for C hinese Named Entity Recognition (2022)

Abstract: Recent years have witnessed the improving performance of Chinese Named Entity Recognition (NER) from proposing new frameworks or incorporating word lexicons. However, the inner composition of entity mentions in character-level Chinese NER has been rarely studied. Actually, most mentions of regular types have strong name regularity. For example, entities end with indicator words such as “公司 (company) ” or “银行 (bank)” usually belong to organization. In this paper, we propose a simple but effective method for investigating the regularity of entity spans in Chinese NER, dubbed as Regularity-Inspired reCOgnition Network (RICON). Specifically, the proposed model consists of two branches: a regularity-aware module and a regularity-agnostic module. The regularity-aware module captures the internal regularity of each span for better entity type prediction, while the regularity-agnostic module is employed to locate the boundary of entities and relieve the excessive attention to span regularity. An orthogonality space is further constructed to encourage two modules to extract different aspects of regularity features. To verify the effectiveness of our method, we conduct extensive experiments on three benchmark datasets and a practical medical dataset. The experimental results show that our RICON significantly outperforms previous state-of-the-art methods, including various lexicon-based methods.

## introduction named entity recognition (ner) aims at identifying text spans pertaining to specific entity types. it plays an important role in many downstream tasks such as relation extraction @xcite , entity linking @xcite , co-reference resolution @xcite , and knowledge graph @xcite . due to the complex composition @xcite , character-level chinese ner * equal contribution † corresponding author is more challenging compared to english ner. as shown in figure 1 (a), the middle character "流" can constitute words with the characters to both their left and their right, such as "河流 (river)" and "流经 (flows)", leading to ambiguous character boundaries. there are two typical frameworks for ner. the first one conceptualizes ner as a sequence labeling task @xcite @xcite , where each character is assigned to a special label (e.g., b-loc, i-loc). the second one is span-based method @xcite , which classifies candidate spans based on their span-level representations. however, despite the success of these two types of methods, they do not explicitly take the complex composition of chinese ner into consideration. recently, several works @xcite @xcite utilize external lexicon knowledge to help connect related characters and promote capturing the local composition. nevertheless, building the lexicon is time-consuming and the quality of the lexicon may not be satisfied. in contrast to previous works, we observe that the regularity exists in the common ner types (e.g., org and loc). as shown in figure 1 (a) , "尼日尔河 (niger river)" follows the specific composition pattern "xx+河 (xx + river)" which ends with indicator character "河" and mostly belongs to location type, and the ambiguous character "流" can properly constitute "流经" with the right character "经". thus, the regularity information serves as important clues for entity type recognition and identifying the character composition. formally, we refer to regularity as specific internal patterns contained in a type of entity @xcite . however, too immersed regularity leads to unfavorable boundary detection of entities and disturbing character composition. as shown in figure 1 (b), "中国队 (chinese team)" conforms to the pattern "xx+队 (xx + team)", but the correct entity boundary should be "中国 (chinese)" and "队员 (players)" according to the context. therefore, the context also plays a key role in determining the character boundary. in this paper, we introduce a simple but effective method to explore the regularity information of entity spans for chinese ner, dubbed as regularity-inspired recognition network (ricon). the proposed model consists of two branches named regularity-aware module and regularity-agnostic module, where each module has task-specific encoder and optimization object. concretely, the regularity-aware module aims at analyzing the internal regularity of each span and integrates the significant regularity information into the corresponding span-level representation, leading to precise entity type prediction. meanwhile, the regularityagnostic module is devised to capture context information and avoid excessive focus on intra-span regularity. furthermore, we adopt an orthogonality space restriction to encourage two branches to extract different features with regard to the regularity. to verify the effectiveness of our method, we conduct extensive experiments on three large-scale benchmark datasets (ontonotes v4.0, ontonotes v5.0, and msra). the results show that ricon achieves considerable improvements compared to the state-of-the-art models, even outperforming existing lexicon-based models. moreover, we experiment on a practical medical dataset (cblue) to further demonstrate the ability of ricon. our contributions can be summarized as follows: ## related work traditional methods treat ner as a sequence labeling task, where each word or character in the sentence is assigned to a special label. as a representative, @xcite utilized the bilstm as an encoder to learn the contextual representation, and then exploited conditional random field (crf) as a decoder to label the tokens. the bilstm-crf architecture achieved superior performance on various datasets, hence many following works @xcite adopt such architecture. more recently, strong pre-trained language models such as elmo @xcite and bert @xcite are incorporated to further enhance the performance of ner. although the sequence labeling framework achieves decent performance on flat ner, it struggles for nested ner. as a result, span-based models are proposed to solve the nested problem by classifying all possible spans into predefined types (e.g. per, loc) in the sentence. for example, @xcite adopted a biaffine attention model to assign scores for all potential spans and achieved the state-of-the-art performance on both flat and nested english ner datasets. @xcite also employed spanbased framework on chinese ner datasets. in this paper, we adopt span-based method as our basic framework for two reasons. firstly, the span-based method considers each span and naturally suits analyzing inner-span character composition. secondly, the span-based framework can easily extend our method from flat ner to nested ner. recently, for chinese ner, researchers proposed various lexicon-based models that incorporate the external lexicon information and obtained better results. @xcite investigated lattice-lstm for incorporating word lexicons into the character-based ner model. however, the lattice structure fails to compute in parallel. to address this problem, @xcite introduced a lexicon-based graph neural network that recasts chinese ner as a node classification task. there are also several works that focus on incorporating all matched words from the lexicon into the character embeddings @xcite . different from the aforementioned lexicon-based works that incorporate external resources, in this paper, we focus on exploring the internal regularity information of spans. ## method the overall architecture of our ricon is shown in figure 2 , which mainly consists of two branches: the regularity-aware module and the regularityagnostic module.