一句话：提出查询学习算法，可同时学习结构无歧义概率上下文无关文法的文法拓扑与概率权重。
识别概率上下文无关文法包括确定文法拓扑与估计规则概率权重两个问题；由于一般上下文无关文法及概率文法学习的困难性，已有工作多关注后者。本文转而处理前者，聚焦结构无歧义的加权上下文无关文法（SUWCFG）与概率上下文无关文法（SUPCFG）。作者证明 SUWCFG 可由共线多重树自动机（CMTA）表示，给出多项式时间学习 CMTA 的查询算法，并说明学到的 CMTA 可转换为概率文法。由此得到完整算法：利用结构化成员查询与结构化等价查询，同时学习 SUPCFG 的拓扑和概率权重。
The problem of identifying a probabilistic context free grammar has two aspects: the first is determining the grammar's topology (the rules of the grammar) and the second is estimating probabilistic weights for each rule. Given the hardness results for learning context-free grammars in general, and probabilistic grammars in particular, most of the literature has concentrated on the second problem. In this work we address the first problem. We restrict attention to structurally unambiguous weighted context-free grammars (SUWCFG) and provide a query learning algorithm for \structurally unambiguous probabilistic context-free grammars (SUPCFG). We show that SUWCFG can be represented using \emph{co-linear multiplicity tree automata} (CMTA), and provide a polynomial learning algorithm that learns CMTAs. We show that the learned CMTA can be converted into a probabilistic grammar, thus providing a complete algorithm for learning a structurally unambiguous probabilistic context free grammar (both the grammar topology and the probabilistic weights) using structured membership queries and structured equivalence queries. A summarized version of this work was published at AAAI 21.
@article{paperbot2240,
title = {Learning of Structurally Unambiguous Probabilistic Grammars},
author = {Dana Fisman and Dolav Nitay and Michal Ziv-Ukelson},
journal = {Logical Methods in Computer Science},
volume = {Volume 19, Issue 1},
year = {2023},
doi = {10.46298/lmcs-19(1:10)2023}
}