## 2 Related Work  
Most NLP technologies develop first with English, then German, French, Chinese, and then low-resource languages such as Tibetan. The research for SBD tasks lies in new features and models that effectively distinguish between bounded and unbounded. For languages such as English, where NLP research has been conducted earlier, most SBD is based on machine learning methods such as Decision Tree (DT) [30], Multilayer Perceptron (MLP) [31], Hidden Markov Model (HMM) [32], maximum entropy (ME) [33], Conditional Random Field (CRF) [34], and other models [35]. NLP techniques for languages such as English provide ideas and references for the study of Tibetan, and the study of English SBD is mainly concerned with determining whether an abbreviated period (\".\") is an actual sentence boundary or just an abbreviation representing a particular position. Scholars have conducted studies based on rule-based and statistical learning methods to address this issue. [PERSON] et al. [36] counted 75,000 English scientific abstracts in which 54.7-92.8% of the sentence dots appeared at the end,about 90% indicated the end of the sentence, 10% indicated abbreviation, and 0.5% both. [PERSON] [37], based on determining whether the words to the left of a potential sentence boundary or the right side of a word are an abbreviation or a proprietary name, uses a set of rules to implement SBD. [PERSON] [38] combines the approach in the literature [37] with lexical tagging methods for supervised learning, including tagging for sentence endings, to reduce the error rate. [PERSON] and [PERSON] [39] propose a completely unsupervised system called PUNKT. The system is rooted in recognizing acronyms by finding collocation keys between candidate words and sentence points. [PERSON] [40] proposed a DT classifier to determine whether acronyms mark sentence boundaries. It utilizes the probability of a word being the end or beginning of a sentence, word length, and word case as features to perform SBD. [PERSON] et al. [41] used supervised ME learning to study SBD by considering sentence segmentation as a disambiguation task with good results. [PERSON] et al. [42] used SVM to study SBD in English by using a large amount of training data through an SVM model to determine the function of abbreviated periods in English. For Tibetan, rule-based research, machine learning, and a combination of both have been applied to the study of SBD. A rule-based approach based on auxiliary suffixes to detect sentence boundaries in Tibetan was proposed by [PERSON] et al. [43], which provided a preliminary analysis and exploration of the syntactic features of Tibetan legal texts. [PERSON] and [PERSON] [44] proposed an SBD method for constructing three lexicons (ending word lexicon, non-ending word lexicon, and unique word lexicon), which transforms the SBD problem into a query of to which lexicon the word to the left of a shad (\"[\"]) belongs. [PERSON] [45] proposed a verb-centered binary SBD method based on maximum entropy. First, the maximum entropy model detects Tibetan sentences by grammar rules and thesaurus and then further identifies ambiguous sentences. Based on the literature [45], [PERSON] et al. [46] proposed a rule and maximum entropy-based approach. The ambiguous sentence boundaries are first identified using Tibetan boundary word lists. Then, the maximum entropy model is used to identify the ambiguous sentence boundaries that the rules cannot recognize. This is the first time that rules and machine learning methods have been combined for the Tibetan SBD task. [PERSON] et al. [47] proposed a Tibetan SBD based on linguistic tagging. Firstly, the text is segmented into words and lexically tagged; then, the text is scanned; when scanning a shad (\"[\"]) or a double shad (\"[\"]), it determines whether the word to the left of the shad (\"[\"]) or double shad (\"[\"]) is a conjunction or not and whether the position of the word is a noun, a numeral, or a state word, if yes, the model will continue scanning; otherwise, sentence segmentation is performed.