Concrete Models and Empirical Evaluations for the Categorical Compositional Distributional Model of Meaning
Concrete Models and Empirical Evaluations for the Categorical Compositional Distributional Model of Meaning
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DOI：
10.1162/coli_a_00209
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发表时间：
2015-03
影响因子：
9.3
通讯作者：
Edward Grefenstette;M. Sadrzadeh
中科院分区：
文献类型：
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作者：
Edward Grefenstette;M. Sadrzadeh
使用经验分布方法建模句子的成分意义一直是计算语言学家的挑战。Clark，Coecke，and Sadrzadeh（2008）和Coecke，Sadrzadeh，and Clark（2010）的范畴模型通过统一范畴语法和意义的分布模型提供了一个解决方案。它考虑到语义向量合成操作过程中的句法关系。但这个设定是抽象的：它没有经过经验数据的评估，也没有应用到任何语言任务中。我们通过开发算法来构建张量和线性映射，并使用经验数据实例化抽象参数，从而为这种设置生成具体模型。然后，我们评估我们的具体模型对几个实验，现有的和新的，基于测量模型如何与人类的判断，在一个释义检测任务。我们的研究结果表明，在这些实验中，这个通用抽象框架的实现与其他领先模型的性能相当或优于其他领先模型。
Modeling compositional meaning for sentences using empirical distributional methods has been a challenge for computational linguists. The categorical model of Clark, Coecke, and Sadrzadeh (2008) and Coecke, Sadrzadeh, and Clark (2010) provides a solution by unifying a categorial grammar and a distributional model of meaning. It takes into account syntactic relations during semantic vector composition operations. But the setting is abstract: It has not been evaluated on empirical data and applied to any language tasks. We generate concrete models for this setting by developing algorithms to construct tensors and linear maps and instantiate the abstract parameters using empirical data. We then evaluate our concrete models against several experiments, both existing and new, based on measuring how well models align with human judgments in a paraphrase detection task. Our results show the implementation of this general abstract framework to perform on par with or outperform other leading models in these experiments.1