PAI at S em E val-2023 Task 2: A Universal System for Named Entity Recognition with External Entity Information (2023)

Abstract: The MultiCoNER II task aims to detect complex, ambiguous, and fine-grained named entities in low-context situations and noisy scenarios like the presence of spelling mistakes and typos for multiple languages. The task poses significant challenges due to the scarcity of contextual information, the high granularity of the entities(up to 33 classes), and the interference of noisy data. To address these issues, our team PAI proposes a universal Named Entity Recognition (NER) system that integrates external entity information to improve performance. Specifically, our system retrieves entities with properties from the knowledge base (i.e. Wikipedia) for a given text, then concatenates entity information with the input sentence and feeds it into Transformer-based models. Finally, our system wins 2 first places, 4 second places, and 1 third place out of 13 tracks. The code is publicly available at https://github.com/diqiuzhuanzhuan/semeval-2023 .

## introduction the objective of the multiconer ii shared task @xcite is to develop a robust named entity recognition (ner) system for multiple languages (including english, spanish, hindi, bangla, chinese, swedish, farsi, french, italian, portuguese, ukranian, german and aforementioned mixed languages) that can perform well even when the input data has spelling errors, lacks contextual information, or contains out of knowledge base entities. this task entails the recognition of a total of 33 fine-grained entity types within six coarsegrained entity categories. the entity types within each category are highly prone to confusion and ambiguity, and the testing data includes noise such as spelling errors that must be considered. similar to the previous competition @xcite , the data in this competition contains a lot of short * these authors contributed equally to this work. and low-context sentences, which means the accurate prediction of entity types relies heavily on external knowledge. therefore, the key objective of our current work is to achieve effective integration of the model and external knowledge. in this paper, extending our previous work @xcite , which has demonstrated the efficacy of the dictionary-fused model, we propose a ner system based on entity property knowledge base. we constructed our knowledge base using all entities and their associated properties (including entity name, alias, sitelink title, description, instanceof, subclassof and occupation) from wikidata. in the retrieval module, we use string matching to retrieve entities with properties for a given sentence. given the sequence length limitations of our model, we keep longer entities in sentences. subsequently, we put the entity and property values into the external context in a specific format and feed the external context and original sentence into the ner model. figure 1 illustrates our motivation, demonstrating how the inclusion of property values can effectively improve performance in practice. we further propose entity-aware attention mechanism, which can better model the semantic relationships between entities in sentences and external context than conventional attention. in the test phase, we use the voting strategy to form the final predictions. we make the following observations according to our experiments: 1. the system based on entity property knowledge base has significantly improved the effect. the knowledge base has a high entity coverage, and even more than 95 percent in some low-resource languages such as bangla and hindi. property can improve the recognition of finegrained person entities. ## related work complex named entities (ne), like the titles of creative works, are not simple nouns and pose challenges for ner systems @xcite . to mitigate these challenges, ner models utilizing external knowledge have achieved remarkable results. researchers have integrated gazetteers into models @xcite in earlier studies. recently, gemnet @xcite proposes a novel approach for gazetteer information integration, where a flexible contextual gazetteer representation (cgr) encoder can be fused with any word-level model and a mixture-of-experts (moe) gating network can overcome the feature overuse issue by learning to conditionally combine the context and gazetteer features, instead of assigning them fixed weights. gain @xcite adapts the representations of gazetteer networks to integrate external entity type information into models. @xcite uses string matching to retrieve entities with types from the gazetteer and concatenates the entity information with the input text and feds it to models. however, the prerequisite for these approaches is to construct an entity dictionary with accurate types, which is extremely difficult to fulfill. knowledge-fused methods mentioned in @xcite build an information retrieval (ir) system based on wikipedia to provide related context information to models. but these methods require retrieving high-quality context, which is difficult to guarantee in the multiconer ii shared task @xcite . actually, transformer-based models @xcite @xcite have the ability to automatically learn the mapping between the entity properties and entity types on the basis of input sentence. therefore, we propose a ner system that fuses entity dictionaries without accurate entity types to overcome these drawbacks of previous work. ## our system in this section, we introduce how our system works. given a sentence @xmath0, x 1 , ..., x n-1 }, x consists of n tokens, and this sentence is input to the knowledge retrieval module. the retrieval module outputs the retrieved entities and property contexts. the system then concatenates the input sentence and outputs from the retrieval module and fed them to a transformer-based model. to enhance the relevance of retrieved entities to the given information, we devised entity-aware attention mechanism. the overall architecture is shown in figure 2 . ## conclusion in this paper, we describe our ner system based on entity properties, which wins two tracks in multi-coner ii shared task. we construct a kb based on entity properties, which is used to retrieve the relevant entity names and contexts for a given sentence. our property dictionary is built without the need for manual relational mapping and achieves high coverage on the test set. we have found that different entity types require different properties. we propose the entity-aware attention mechanism to better learn the relationship between entities and contexts. in the future, we plan to adopt fuzzy matching to improve the performance on noisy data and explore our system for other low-resource tasks.