基于句法树的藏语最长名词短语识别

最长名词短语携带着丰富的句法和语义信息,经常与句法成分对应,在句子中充当一定的语义角色。最长名词短语识别在自然语言处理中占重要地位,是分析和理解句子结构、意义的基础。该文通过梳理不同概念的最长名词短语的含义,从句法树角度界定了藏语最长名词短语的基本概念;从句法树库中抽取6 038个句子,分析了最长名词短语的结构类型、边界特征和出现频次,最后采用序列标注模型和句法分析模型对最长名词短语进行识别。序列标注模型识别结果的正确率、召回率和F₁值分别为87.14%、84.72%、85.92%。句法分析模型识别结果的正确率、召回率、F₁值分别为85.02%、84.51%、84.76%。

Longest Noun Phrases Detection in Tibetan

The longest noun phrases carry abundant syntactic and semantic information, corresponding to a syntactic components for most cased. By comparing the essence of the different longest noun phrases, this paper defines the longest noun phrase in Tibetan language from the perspective of syntactic tree. Total of 6 038 sentences are extracted from a Tibetan treebank, and the structure type, boundary feature and frequency of longest noun phrases are analyzed. Two approaches, the sequence annotation model and the parsing algorithm, are investigated to detect the longest noun phrases in Tibetan. Experiments proves the better performance of the sequence labeling approach, yielding 87.14% precision, 84.72% recall and 85.92% F-value respectively.