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Large-scale evaluation of dependency-based DSMs: Are they worth the effort?

机译：基于依赖项的DSM的大规模评估：值得付出努力吗？

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This paper presents a large-scale evaluation study of dependency-based distributional semantic models. We evaluate dependency-filtered and dependency-structured DSMs in a number of standard semantic similarity tasks, systematically exploring their param eter space in order to give them a "fair shot" against window-based models. Our results show that properly tuned window-based DSMs still outperform the dependency-based models in most tasks. There appears to be little need for the language-dependent resources and computational cost associ ated with syntactic analysis.

机译：本文提出了基于依赖的分布语义模型的大规模评估研究。我们在许多标准的语义相似性任务中评估依赖项过滤和依赖结构的DSM，系统地探索其参数空间，以便针对基于窗口的模型提供“公平的机会”。我们的结果表明，在大多数任务中，经过适当调整的基于窗口的DSM仍然优于基于依赖项的模型。似乎很少需要与语法分析相关的语言相关资源和计算成本。

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《》|2017年|394-400|共7页
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Gabriella Lapesa; Stefan Evert;
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Large-scale evaluation of dependency-based DSMs: Are they worth the effort?

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