Incorporating Linguistics Constraints into Inductive Logic Programming
Incorporating Linguistics Constraints into Inductive Logic Programming
复制标题
将语言学约束纳入归纳逻辑编程
DOI：
10.3115/1117601.1117647
复制
发表时间：
2000
期刊：
影响因子：
--
通讯作者：
S. Pulman
中科院分区：
文献类型：
--
作者：
J. Cussens;S. Pulman
我们报告了将语言知识有效地纳入语法归纳的工作。我们使用一种高度交互的自下而上的归纳逻辑规划（ILP）算法从不完整的语法中学习“缺失”的语法规则。使用语言约束，例如头部特征和间隙线程，在一定程度上减少了搜索空间，在这里报告的小规模实验中，我们可以生成并存储所有候选语法规则及其覆盖范围和语言属性的信息。这允许一种非常简单和可控的方法来生成语言上合理的语法规则。从高度特定规则的基础开始，我们应用最小一般泛化和逆分辨率来生成更一般的规则。诱导规则是有序的，例如按覆盖范围排序，以便于用户检查，并且在任何时候，用户都可以提交假设规则并将其添加到语法中。讨论了ILP和计算语言学的相关工作。
We report work on effectively incorporating linguistic knowledge into grammar induction. We use a highly interactive bottom-up inductive logic programming (ILP) algorithm to learn 'missing' grammar rules from an incomplete grammar. Using linguistic constraints on, for example, head features and gap threading, reduces the search space to such an extent that, in the small-scale experiments reported here, we can generate and store all candidate grammar rules together with information about their coverage and linguistic properties. This allows an appealingly simple and controlled method for generating linguistically plausible grammar rules. Starting from a base of highly specific rules, we apply least general generalisation and inverse resolution to generate more general rules. Induced rules are ordered, for example by coverage, for easy inspection by the user and at any point, the user can commit to a hypothesised rule and add it to the grammar. Related work in ILP and computational linguistics is discussed.