YNUNLP at S em E val-2023 Task 2: The Pseudo Twin Tower Pre-training Model for C hinese Named Entity Recognition (2023)

Abstract: This paper introduces our method in the system for SemEval 2023 Task 2: MultiCoNER II Multilingual Complex Named Entity Recognition, Track 9-Chinese. This task focuses on detecting fine-grained named entities whose data set has a fine-grained taxonomy of 36 NE classes, representing a realistic challenge for NER. In this task, we need to identify entity boundaries and category labels for the six identified categories. We use BERT embedding to represent each character in the original sentence and train CRF-Rdrop to predict named entity categories using the data set provided by the organizer. Our best submission, with a macro average F1 score of 0.5657, ranked 15th out of 22 teams.

## introduction ner (named entity recognition) is a traditional nlp task designed to identify text fragments belonging to predefined categories in free text @xcite . ner is an important basic tool for many nlp tasks such as information extraction, question answering system, syntactic analysis and machine translation. named entities generally refer to entities with specific meaning or strong reference in the text. academically, it usually includes three categories: entity, time and number, and seven categories: personal name, place name, organization name, time, date, currency and percentage @xcite . ner extracts the above entities from the unstructured input text and can identify more categories of entities based on business requirements. chinese named entity recognition @xcite plays an important role in the field of natural language processing. compared with english, chinese ner is more challenging. first of all, chinese word boundaries are vague, and there are no delimiters, such as spaces, to clarify word boundaries. if chinese ner adopts character-level model, there will be semantic loss and boundary information loss. on the other hand, if we use the word-level model, the wrong word segmentation can also degrade performance. there are also more complex properties in chinese, such as complex combinations, entity nesting, indefinite length, and neologisms on the internet. in addition, chinese is not case-sensitive and root-affix, and lacks the expression of a lot of semantic information. named entity recognition is very important because in many applications we must extract the entities in our vocabulary @xcite . semeval 2023 task 2: multiconer ii multilingual complex named entity recognition @xcite . this task aims to solve the problem of fine-grained named entity recognition. the mission has 13 tracks, of which tracks 1-12 are monolingual, including english, spanish, hindi, bangla, chinese, swedish, farsi, french, italian, portuguese, ukrainian and german @xcite . participants trained a model that only worked in one language. track 13 is a multilingual track, where participants need to use data from 12 languages to train a single multilingual ner model for all languages, which should be able to process single-language data from any language. we took part in the chinese monolingual program. ## related work the term named entity recognition first appeared in message understanding conferences (muc-6), which mainly focuses on information extraction @xcite . in addition to information extraction evaluation task, the muc-6 also introduced a new evaluation task namely named entity recognition task. when a task is first proposed, it defines only a few generic entity categories, such as places, agencies, people, and so on. at present, named entity recognition task has penetrated into various vertical fields, such as medical treatment, finance and so on. the main methods of named entity recognition include dictionary and rule based method, traditional machine learning method and deep learning method @xcite . ## methodology in this part, the model adopted by us for chinese program of semeval-2023 task 2 is introduced in detail. this model consists of three parts, which we call the bert-crf-rdrop model. figure 1 shows the architecture of the bert-crf-rdrop model. ## results we use accuracy, recall rate and f1 to evaluate the performance of our proposed model. precision measures the model's ability to present only correct entities, while recall measures the model's ability to identify all entities in the data set. f1 is the harmonic average of precision and recall, which can be calculated as follows: @xmath0 due to the limitation of time and computing resources, we only give the results of the chinese model. ## conclusion this study presents the bert-crf-rdrop system submitted for the semeval2023 task 2: multi-coner chinese track, including system design, implementation and evaluation. to address the challenges of complex, fuzzy, and emerging entity problems, we used bert embedding to represent each character in the original sentence and trained crf-rdrop to predict named entity categories using the data set provided by the organizer. the experimental results show that our pre-training language model is effective in named entity recognition. in future work, we will further improve performance by developing more complex integration strategies and more diverse models.