Descriptive grammars are highly valuable, but writing them is time-consuming
and difficult. Furthermore, while linguists typically use corpora to create
them, grammar descriptions often lack quantitative data. As for formal
grammars, they can be challenging to interpret. In this paper, we propose a new
method to extract and explore significant fine-grained grammar patterns and
potential syntactic grammar rules from treebanks, in order to create an
easy-to-understand corpus-based grammar. More specifically, we extract
descriptions and rules across different languages for two linguistic phenomena,
agreement and word order, using a large search space and paying special
attention to the ranking order of the extracted rules. For that, we use a
linear classifier to extract the most salient features that predict the
linguistic phenomena under study. We associate statistical information to each
rule, and we compare the ranking of the model's results to those of other
quantitative and statistical measures. Our method captures both well-known and
less well-known significant grammar rules in Spanish, French, and Wolof.

我们提出了一种新的方法，从树库中提取和探索显著的细粒度语法模式和潜在的句法语法规则，以创建易于理解的基于语料库的语法描述。我们使用线性分类器提取预测研究中语言现象最显著特征，并将统计信息与每个规则关联，然后将模型结果的排序与其他定量和统计测量结果进行比较。我们的方法捕捉到了西班牙语、法语和沃洛夫语中已知和不太为人知的重要语法规则。