Syntactic Approach for Efficient Identification in the Limit from Positive Data of Context-Free and Mildly Context-Sensitive Languages

近年来的文献表明，上下文无关语言具有反映自然语言现象的一个方面的特殊性质，可以有效地从正面数据中学习。总结之前的研究，我们的项目提出了有效的算法，可以从积极的数据中学习甚至更丰富的上下文无关语言类以及轻度上下文敏感语言类，这些语言处理在自然语言中观察到的一些非上下文无关现象。此外，我们的研究项目提出了一些技术，可以在回答学习者有限问题的老师的帮助下，从积极的数据中学习更具表现力的语言课程。

Recently the literature showed that context-free languages with a special property reflecting an aspect of natural language phenomena are efficiently learnable from positive data. Generalizing the preceding research, our project has presented efficient algorithms that learn from positive data even richer classes of context-free languages as well as those of mildly context-sensitive languages, which handle some non-context-free phenomena observed in natural languages. Moreover, our research project has proposed techniques that learn even more expressive classes of languages from positive data with the aid of a teacher who answers limited questions from the learner.