Efficient Learning of Tier-Based Strictly k-Local Languages
نویسندگان
چکیده
We introduce the Tier-based k-Strictly Local Inference Algorithm (kTSLIA), an algorithm that learns the class of Tier-based Strictly k-Local (TSLk) formal languages in polynomial time on a sample of positive data whose size is bounded by a constant. The TSLk languages are useful in modeling the cognition of sound patterns in natural language [6, 11], and it is known that they can be efficiently learned from positive data in the case that k = 2 [9]. The kTSLIA extends this result to any k and improves on its time efficiency. We also refine the definition of a canonical TSLk grammar and prove several properties about these grammars that aid in their learning.
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