## 1 Introduction  
CWS is one of the fundamental tasks in Chinese NLP [1, 2, 3] and plays a significant role in many NLP tasks such as part-of-speech [4], named entity recognition [5, 6, 4], relation extraction [7, 8, 9], and coreference resolution [10]. For the geoscience domain, CWS functions as a crucial step for NLP of geological texts, using NLP technology to discover new knowledge and advance geological work. However, domains such as general domain [11, 12],electronic medical records [13; 14], and Chinese novels [15; 16] are the focus of the most current research. Compared with the general domain CWS, the geoscience domain CWS also includes professional geoscience terms, such as geomorphology, geological history, stratigraphic distribution, lithology, and structure [17]. Overall, CWS in the geosciences domain has the following primary challenges: (1) The lack of labeled data in the geosciences. The time and cost of the manual annotation process significantly limit the availability of reliable and high-quality annotated data; (2) The strong cross-disciplinary characteristics of terms, such as the biological term \" (angiosperm pollen)\" and the chemical term \" (isotopic age)\", which span different disciplines, posing challenges for word separation; (3) The geological word is often a mixture of Chinese, English, punctuation, and numbers, such as \" (zircon U-Pb ages)\" and \" (Fanwei village SW12 borehole)\", making it difficult to be parsed. Therefore, understanding domain-specific meanings of terms within the context and background of geoscience is particularly important for CWS models in the geoscience domain compared to CWS models in the general domain. Recently, due to advancements in deep learning technology, deep learning models [18] have progressively overtaken dictionary-based CWS approaches [19] and statistics-based CWS methods [20]. Specifically, with the introduction of pre-trained language models, such as ELMo [21] and BERT [22], several improved models based on pre-trained language models [17; 23; 24] have been applied to CWS tasks in the geoscience domain. However, these deep learning methods rely on predetermined domain-specific dictionaries and rules. These methods contain the following two problems when used for CWS in the geoscience domain: (1) Because the predetermined domain dictionaries and rules do not always cover the diverse and evolving terminology present in geoscience domains, the diversity of geological terms may lead to ambiguous word segmentation. Moreover, the process of constructing domain knowledge is still time-consuming and labor-intensive; (2) Pretrained language models, such as BERT, face difficulties in processing long or nested words. Thus, they are unable to effectively identify word boundaries while working with complex geological terms. For example, \" (Medium-fine grained porphyritic biotite Granite)\" will be extracted as \" (Medium-fine grained)\", \" (Like)\", \" ( porphyritic)\", \" (black cloud)\" and \" (Granite)\". To address the aforementioned problems, we propose the following solutions:  
1. We propose a backbone model that incorporates pseudo-lexicon with BERT to solve the problem of identifying complex geological word boundaries. Compared to using an off-the-shelf or manual lexicon, the pseudo-lexicon, which is automatically constructed and updated based on the dataset, can be better adapted to different data scenarios. We construct a pseudo-lexicon by n-gram measures and then input characters into the key-value memory network module to match the pseudo-lexicon-based memory. After the matching process, we extract the domain-enhanced features of characters by encoding pseudo-lexicon and its position patterns as boundary information;
2. We train the backbone model with the uncertainty-aware self-training strategy to enhance the generalization capacity of the model in the absence of predetermined domain knowledge.