Frequently corresponding to syntactic components, the Maximal-length Noun Phrase (MNP) possesses abundant syntactic and semantic information and acts a certain semantic role in sentences. Recognition of MNP plays an important role in Natural Language Processing and lays the foundation for analyzing and understanding sentence structure and semantics. By comparing the essence of different MNPs, this article defines the MNP in the Tibetan language from the perspective of syntax tree. A total of 6,038 sentences are extracted from the syntax tree corpus, the structure type, boundary feature, and frequency of MNPs are analyzed, and the MNPs are recognized by applying the sequence tagging model and the syntactic analysis model. The accuracy, recall, and F1 score of the recognition results of applying sequence tagging model are 87.14%, 84.72%, and 85.92%, respectively. The accuracy, recall, and F1 score of the recognition results of applying syntactic analysis model are 87.66%, 87.63%, and 87.65%, respectively.

中文翻译：

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基于句法树的藏文最长名词短语识别

最大长度名词短语（Maximal-length Noun Phrase，MNP）通常与句法成分相对应，具有丰富的句法和语义信息，在句子中起到一定的语义作用。MNP的识别在自然语言处理中发挥着重要作用，为分析和理解句子结构和语义奠定了基础。通过比较不同MNP的本质，本文从句法树的角度对藏语中的MNP进行了定义。从句法树语料库中提取6038个句子，分析MNPs的结构类型、边界特征和频率，应用序列标注模型和句法分析模型识别MNPs。应用序列标注模型的识别结果的准确率、召回率和F1分数分别为87.14%、84.72%和85.92%，分别。应用句法分析模型的识别结果的准确率、召回率和F1分数分别为87.66%、87.63%和87.65%。