# Multi-Meta Information Embedding Enhanced BERT for Chinese Mechanics Entity Recognition
## 1 Introduction  
With the rapid development of artificial intelligence (AI), the research on intelligent tutoring algorithms (ITAs) has also made great progress [1; 2; 3]. The most important link in developing ITA for mechanics problems is problem understanding [4]. The purpose of problem understanding is to convert input problems into structured given conditions through natural language processing (NLP). Nevertheless, as mechanics problems usually involve a large number of complex relations (nested relations and overlapping relations), existing methods utilize named entity recognition (NER) to accomplish the problem understanding task [4; 5]. NER is the basis for developing NLP applications [6], such as information extraction (IE) [7; 8; 9], knowledge extraction (KE) [10; 11], question answering (QA) [12; 13], and intelligent tutoring systems (ITSs) [3, 4]. The main task of NER is to extract entities in unstructured text and classify the elements into predefined categories such as name, organization, location, and string value. Recognizing named entities for mechanics problems can acquire the basic information and given conditions of the input problem, which provide support for automatically finding the answer. Therefore, it is of great significance to accurately extract named entities for mechanics problems. From the perspective of application scenarios, our purpose was to identify the key entities of input mechanics problems. For instance, the green characters are the key entities of the example provided in Figure 1. However, developing an efficient model for this task faces some challenges. In contrast to English, Chinese sentences have no boundaries for word segmentation [14]. Therefore, early Chinese named entity recognition (CNER) methods only took characters as input [15]. Since lexical boundaries are usually the same as entity boundaries, scholars have paid increasing attention to developing algorithms utilizing lexical information [16]. With continuous exploration, many innovational methods have been proposed. These methods can be divided into three categories. The first approach is the pipeline model. This type of method performs Chinese word segmentation (CWS) first and then takes these segmented sequences as the input. However, these methods ignore the influence of word segmentation, which easily causes error propagation and reduces the recognition performance. The second approach is the multi-task model. Inspired by multi-task learning, this type of method usually trains CWS and CNER jointly. However, these methods make fully mining the learning ability of the model difficult, and the generalization ability is usually poor. The last approach is the character representation enhancing model. This type of method usually enhances the character representation with lexical information before feeding it into the main model. Compared with the aforementioned two methods, the third approach can obtain better performance. The current state-of-the-art CNER algorithm was based on the third approach. The model proposed in this article also belongs to this approach. In addition to the lexical segmentation problem, there are three challenges in identifying entities from mechanics problems. Firstly, as provided in Figure 1, the target text contains much terminology, and only some of these terms are target entities. For instance, as provided in Figure 1, \"\(\exists\)\" (speed)\", \"\(\exists\)\" (uniform deceleration linear motion)\", and \"\(\exists\)\" (motion)\" are terminology. Nonetheless, only \"\(\exists\)\" (uniform deceleration linear motion)\" is the target entity. Although large-scale language models, such as bidirectional encoder representation from transformers (BERT), can enhance the character representation, they tend to ignore the fact that learning character terminology information directly can bring further improvement. The boundary information, semantic information, and contextual information of lexicons are a benefit to distinguishing target entities accurately.