CASIA at S em E val-2022 Task 11: C hinese Named Entity Recognition for Complex and Ambiguous Entities (2022)

Abstract: This paper describes our approach to develop a complex named entity recognition system in SemEval 2022 Task 11: MultiCoNER Multilingual Complex Named Entity Recognition,Track 9 - Chinese. In this task, we need to identify the entity boundaries and categorylabels for the six identified categories of CW,LOC, PER, GRP, CORP, and PORD.The task focuses on detecting semantically ambiguous and complex entities in short and low-context settings. We constructed a hybrid system based on Roberta-large model with three training mechanisms and a series of data gugmentation.Three training mechanisms include adversarial training, Child-Tuning training, and continued pre-training. The core idea of the hybrid system is to improve the performance of the model in complex environments by introducing more domain knowledge through data augmentation and continuing pre-training domain adaptation of the model. Our proposed method in this paper achieves a macro-F1 of 0.797 on the final test set, ranking second.

## introduction semeval-2022 task 11: multiconer multilingual complex named entities recognition @xcite . this task aims to address the problem of complex and ambiguous named entities in practical and open domain environments . the task has 13 tracks, with track 1 being a multilingual track where participants need to train a multilingual model using data from 11 languages. the model should be able to handle monolingual data from any of the languages and code-mixed cases. tracks 2-12 are monolingual tracks, including english, spanish, dutch, russian, turkish, korean, farsi, german, chinese, hindi and bengali. participants are required to train a model for only one language. track 13 is code-mixed @xcite . this test data contains code-mixed samples. these samples include tokens from any of the 11 mentioned languages in the shared task. we participated in the chinese monolingual track. dealing with complex and ambiguous entities in practical and open domain environments is a challenging nlp task @xcite ,however, which has not received sufficient attention from the research community. the challenges of this task are mainly in three facts: 1)the complex entities problem, in which complex noun phrases, verbs, infinitives, or complete sentences, and other entities lack proper nouns, making it difficult to identify them @xcite .2)the ambiguous entities problem, in which some words are entities in some fields but not in others, especially in search, asr (automatic speech recognition), and other fields.3)the emerging entities problem, such as books, songs and movies, new works are released every week, and the problem in these fields is that the entities are growing faster, and it is more difficult to identify these newly growing entities. for the complex entities problem, we use adversarial training and child-tuning to increase the representation capability at the parameter level, preventing the model from overfitting due to complex entities, so that the model does not simply remember the complex entities. for the ambiguous entities problem, we use a context-independent data augmentation strategy to replace entities in the data for semantic augmentation, so that the model can reduce the context dependency.for the emerging entities problem, we propose a progressive domainadaptive pre-training mechanism to improve the performance of the model, so that the preceding methods yielded considerable results, with the f1 value in the chinese monolingual track reaching 0.797. ## case study our model undergoes the above approach and the above challenges are effectively improved. it is able to identify ambiguous and complex entities in shorter contexts.the case study is shown in as you can see, our method can easily predict entities like creative work compared to baseline and other methods. ## conclusion and future work to address the challenges of complex entities, ambiguous entities and emerging entities problem, we propose adversarial training and child-tuning training methods, context-independent data augmentation strategies, and a progressive domainadaptive pre-training mechanism to improve the performance of the named entity recognition system. in the future, we will focus our efforts on strategies and methods to enhance the use of data and hope to make a good progress.