Abstract
Learning of (context-free) grammar rules that are based on alignment between texts of a given collection of sentences has attracted the attention of many researchers. We define and study the alignment profile and formulate fuzzy similarity of alignment profiles for a given collection of sentences. Using the fuzzy-similarity-based profile alignment, we give a methodology to formulate stochastic context-free grammar (CFG) rules. We introduce profile-alignment-based dynamic sentence similarity threshold to formulate the rules of stochastic CFG. The proposed methodology is tested using Child Language Data Exchange System (CHILDES) dataset of sentences. The benefits of our approach are experimentally demonstrated. Since our approach does not make use of any domain knowledge, it is expected to be useful in wide variety of applications requiring model construction.
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