Abstract
This paper describes a formalization based on tree automata for incremental learning of context-free grammars from positive samples of their structural descriptions. A structural description of a context-free grammar is a derivation tree of the grammar in which labels are removed. The tree automata based learning in this paradigm is early introduced by Sakakibara in 1992, however his scheme assumes that all training examples are available to the learning algorithm at the beginning (i.e., it cannot be employed as an online learning) and also it doesn’t optimize the storage requirements as well. Our model has several desirable features that runs in O(n 3) time in the sum of the sizes of the input examples, obtains O(n) storage space saving, achieves good incremental behavior by updating a guess incrementally and infers a grammar from positive-only examples efficiently. Several examples and experimental results are given to illustrate the scheme and its efficient execution.
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Prajapati, G.L., Chaudhari, N.S., Chandwani, M. (2008). Efficient Incremental Model for Learning Context-Free Grammars from Positive Structural Examples. In: Darzentas, J., Vouros, G.A., Vosinakis, S., Arnellos, A. (eds) Artificial Intelligence: Theories, Models and Applications. SETN 2008. Lecture Notes in Computer Science(), vol 5138. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87881-0_23
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