Learning Context-Free Grammars with a Simplicity Bias
Learning Context-Free Grammars with a Simplicity Bias
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带着简单性偏见学习上下文无关语法
DOI：
10.1007/3-540-45164-1_23
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发表时间：
2000
期刊：
影响因子：
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通讯作者：
Sean Stromsten
中科院分区：
文献类型：
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作者：
P. Langley;Sean Stromsten
我们研究了简单性在指导从样句中归纳上下文无关语法中的作用。我们提出了Wolff SNPR的合理重建-网格系统-它包含了对最小化描述长度的语法的偏见。该算法在合并现有的非终结符和创建新符号之间交替进行，使用波束搜索从复杂的语法转向更简单的语法。实验表明，这种方法可以归纳出准确的语法，并且可以合理地扩展到更困难的领域。
We examine the role of simplicity in directing the induction of context-free grammars from sample sentences. We present a rational reconstruction of Wolff’s SNPR — the Grids system — which incorporates a bias toward grammars that minimize description length. The algorithm alternates between merging existing nonterminal symbols and creating new symbols, using a beam search to move from complex to simpler grammars. Experiments suggest that this approach can induce accurate grammars and that it scales reasonably to more difficult domains.