ABSTRACT

Measuring the semantic similarity and relatedness of words can play a vital role in many natural language processing tasks. Distributional semantic models computing these measures can have many different parameters, such as different weighting schemes, vector similarity measures, feature transformation functions and dimensionality reduction techniques. Despite their importance there is no truly comprehensive study simultaneously evaluating the numerous parameters of such models, while also considering the interaction of these parameters with each other.

We would like to address this gap with our systematic study. Taking the necessary distributional information extracted from the chosen dataset as already granted, we evaluate all important aspects of the creation and comparison of feature vectors in distributional semantic models. Testing altogether 10 parameters simultaneously, we try to find the best combination of parameter settings, with a large number of settings examined in case of some of the parameters. Beside evaluating the conventionally used settings for the parameters, we also propose numerous novel variants, as well as novel combinations of parameter settings, some of which significantly outperform the combinations of settings in general use, thus achieving state-of-the-art results.

摘要

测量单词的语义相似性和相关性可以在许多自然语言处理任务中发挥至关重要的作用。计算这些度量的分布式语义模型可以具有许多不同的参数，例如不同的加权方案，向量相似性度量，特征转换函数和降维技术。尽管它们很重要，但是还没有真正全面的研究同时评估此类模型的众多参数，同时也考虑了这些参数之间的相互作用。

我们希望通过系统的研究来弥补这一差距。考虑到已经从选定的数据集中提取必要的分布信息，我们评估了分布语义模型中特征向量的创建和比较的所有重要方面。同时测试总共10个参数，我们尝试找到最佳的参数设置组合，并在某些参数的情况下检查了大量的设置。除了评估常规使用的参数设置外，我们还提出了许多新颖的变体以及参数设置的新颖组合，其中一些显着优于一般使用的组合设置，从而获得了最新的技术成果。