Probabilistic learnability of context-free grammars with basic distributional properties from positive examples
نویسندگان
چکیده
Article history: Available online xxxx
منابع مشابه
Grammatical Inference of some Probabilistic Context-Free Grammars from Positive Data using Minimum Satisfiability
Recently, different theoretical learning results have been found for a variety of context-free grammar subclasses through the use of distributional learning (Clark, 2010b). However, these results are still not extended to probabilistic grammars. In this work, we give a practical algorithm, with some proven properties, that learns a subclass of probabilistic grammars from positive data. A minimu...
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Context-free grammars cannot be identified in the limit from positive examples (Gold, 1967), yet natural language grammars are more powerful than context-free grammars and humans learn them with remarkable ease from positive examples (Marcus, 1993). Identifiability results for formal languages ignore a potentially powerful source of information available to learners of natural languages, namely...
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Context-free grammars cannot be identified in the limit from positive examples (Gold 1967), yet natural language grammars are more powerful than context-free grammars and humans learn them with remarkable ease from positive examples (Marcus 1993). Identifiability results for formal languages ignore a potentially powerful source of information available to learners of natural languages, namely, ...
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This paper investigates the learnability by positive examples in the sense of Gold of Pregroup Grammars. In a first part, Pregroup Grammars are presented and a new parsing strategy is proposed. Then, theoretical learnability and non-learnability results for subclasses of Pregroup Grammars are proved. In the last two parts, we focus on learning Pregroup Grammars from a special kind of input call...
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ورودعنوان ژورنال:
- Theor. Comput. Sci.
دوره 620 شماره
صفحات -
تاریخ انتشار 2016