N et E ase. AI at S em E val-2023 Task 2: Enhancing Complex Named Entities Recognition in Noisy Scenarios via Text Error Correction and External Knowledge (2023)

Abstract: Complex named entities (NE), like the titles of creative works, are not simple nouns and pose challenges for NER systems. In the SemEval 2023, Task 2: MultiCoNER II was proposed, whose goal is to recognize complex entities against out of knowledge-base entities and noisy scenarios. To address the challenges posed by MultiCoNER II, our team NetEase.AI proposed an entity recognition system that integrates text error correction system and external knowledge, which can recognize entities in scenes that contain entities out of knowledge base and text with noise. Upon receiving an input sentence, our systems will correct the sentence, extract the entities in the sentence as candidate set using the entity recognition model that incorporates the gazetteer information, and then use the external knowledge to classify the candidate entities to obtain entity type features. Finally, our system fused the multi-dimensional features of the candidate entities into a stacking model, which was used to select the correct entities from the candidate set as the final output. Our system exhibited good noise resistance and excellent entity recognition performance, resulting in our team’s first place victory in the Chinese track of MultiCoNER II.

## introduction named entity recognition (ner) is a core natural language processing (nlp) task that aims to detect and classify entities in a sequence. in practical and open-domain settings, however, it is difficult for machines to process complex and ambiguous named entities @xcite , and the performance of the ner system can be significantly reduced when the text contains noise (such as spelling errors). to alleviate this issue, semeval 2023 task 2 @xcite was formulated to focus on recognizing semantically ambiguous and complex entities against out of knowledge-base entities and noisy scenarios. in practice, noise in the text leads to deviation of the text semantics. therefore, it is often necessary to employ some methods to correct the erroneous text in order to minimize the semantic distortion and its impact on the textual understanding. when labeling complex entities, people often rely on external knowledge to aid them. several studies have found that incorporating prior knowledge is beneficial for entity recognition @xcite @xcite . therefore, we believe that correcting noise and leveraging external knowledge can help to recognize entities in noisy text. to effectively combine all the features, we use a stacking model @xcite to integrate the semantic features of the text and external knowledge features for entity recognition. in this article, our system consists of multiple modules. after the text is inputted, the text error correction module is used to eliminate noise. then, the entity recognition module performs the entity recognition task on the corrected text and generates the span and category of the candidate entity set. subsequently, the entity span is fed into the knowledge-based classifier module to gain the entity related information from the knowledge system and obtain the entity type by the classifier. finally, the candidate entity span and entity type predicted by multiple modules are inputted into the stacking model to comprehensively assess the correctness of the entity and output the correct entity judged by the model. ## our system this section will introduce the various parts of our system and how the system works. figure 1 shows composition of our system. ## conclusion in this article, we present our system for the shared task, which won the championship in the chinese track and far outperformed the second in the final score. to address the problem of text noise and complex entities out of knowledge base raised by the task, we designed a text correction module to strategy macro@f1 a 0.7817 b 0.8252 c 0.8676 d 0.9005 correct text errors. we leveraged graph convolutional network to construct a gazetteer with external knowledge to enhance the performance of the entity recognition model. also, we employed a text classification model based on external knowledge to improve the accuracy of the entity type classification. finally, we integrated multiple features into a stacking model to output the correct entities. our experiments demonstrate that our ner system is able to address the complex entity recognition problem by constructing a gazetteer and utilizing knowledge retrieval. additionally, we have demonstrated that building an effective text correction model can reduce the impact of text noise. the stacking model enables the integration of features of diverse dimensions, thereby effectively improving the performance of our entity recognition system. for future work, we intend to enhance the entity recognition performance by improving the accuracy of gazetteer construction and optimizing entity retrieval.