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Dependency Parsing with Bounded Block Degree and Well-nestedness via Lagrangian Relaxation and Branch-and-Bound

机译:通过拉格朗日松弛和分支定界分析有界块度和良好嵌套的依存关系

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We present a novel dependency parsing method which enforces two structural properties on dependency trees: bounded block degree and well-nestedness. These properties are useful to better represent the set of admissible dependency structures in treebanks and connect dependency parsing to context-sensitive grammatical formalisms. We cast this problem as an Integer Linear Program that we solve with Lagrangian Relaxation from which we derive a heuristic and an exact method based on a Branch-and-Bound search. Experimentally, we see that these methods are efficient and competitive compared to a baseline unconstrained parser, while enforcing structural properties in all cases.
机译:我们提出了一种新颖的依赖项解析方法,该方法在依赖项树上实现了两个结构属性:有界块度和良好的嵌套度。这些属性有助于更好地表示树库中可允许的依赖结构集,并将依赖关系分析与上下文相关的语法形式主义联系起来。我们将此问题转换为整数线性程序,使用拉格朗日松弛法求解,从中得出基于分支定界搜索的启发式方法和精确方法。从实验上看,我们发现与基线无约束解析器相比,这些方法是有效且具有竞争力的,同时在所有情况下都可以强制执行结构属性。

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