# Medical Named Entity Recognition Fusing Part-of-Speech and Stroke Features
## 1 Introduction  
With the rise of \"Internet + Medical\", major online medical service platforms bring convenience to people and create a significant volume of text data containing rich medical knowledge. Most of these text data are in an unstructured form, in which it is difficult to intuitively reflect information. Processing and mining these textual data can help professionals quickly and accurately obtain information to assist in diagnostic decision-making, save time and cost, and better provide treatment for patients. The aim of named entity recognition (NER) is to find entities in a context that have specific meanings. NER extracts and classifies the names of people, places, institutions, biological proteins, drugs, and other entities in unstructured text to serve downstream tasks including text mining, information extraction, intelligent question answering, machine translation, and so on [1]. Numerous scholars have studied entity recognition in a variety of domains, including chemistry [2, 3], politics and law [4, 5], social media [6, 7], and geography [8], thanks to the ongoing advancement of NER technology. [PERSON] et al. [2] presented an Att-BiLSTM-CRF model for chemistry NER, which applies attention to the document to determine the similarity between various words in the document and assigning weights. [PERSON] et al. [4] introduced a legal judgment named the entity recognition approach based on small-scale tag data, which uses bootstrapped mode to learn how tolabel data from small-scale manually labeled data, and then embed words with the Chinese model pre-trained by BERT. [PERSON] et al. [6] introduced a multitasking approach applied to social media data, which combines named entity segmentation with fine-grained network element classification main tasks. Within the biomedical field, [PERSON] et al. [9] suggested an end-to-end deep learning method called GRAM-CNN, which utilizes local context embedded by n-gram characters and words via convolutional neural networks without requiring any specific knowledge or feature engineering. [PERSON] et al. [10] suggested a model known as CollaboNet that combines many NER models. The target model can obtain information from other cooperation models thanks to the connections between models trained on various datasets, which lowers the number of false positives and incorrectly categorized entities. Unlike languages such as English, Chinese lacks a distinct demarcation between words, and the grammatical structure of Chinese is complex and diverse. Chinese medical entities usually contain a large number of professional words and terms, making it challenging to understand and analyze these complex language structures for the model. And the technical terms involved in the medical field may have different forms and variants such as synonyms, abbreviations, spelling mistakes, etc. Additionally, The length of medical entities, such as \" (radical left hemicolectomy)\", is excessively long. And the nested nature of various entity types, for instance, the imaging examination entity \" (gastrointestinal tract iodine hydrography)\" nested in the symptom entity \" (gastrointestinal tract)\". These problems make it more difficult for Chinese medical entity recognition. Based on the aforementioned issues, this research suggests a named entity recognition model fusing part-of-speech and stroke features for medical texts. Firstly, in order to discriminate words with distinct meanings in the text, word parts-of-speech and stroke information are incorporated as multiple characteristics in low-dimensional word vectors acquired by the BERT pre-training model, improving the border recognition result, in which stroke features are extracted by Convolution Neural Network (CNN). Following that, word vectors are spliced with the parts-of-speech and stroke vectors, respectively, to be input into the bidirectional short-term memory network (BiLSTM) for feature extraction.