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Graph-Based Unsupervised Learning of Word Similarities Using Heterogeneous Feature Types

机译:使用异类特征类型的基于图的词相似度无监督学习

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In this work, we propose a graph-based approach to computing similarities between words in an unsupervised manner, and take advantage of heterogeneous feature types in the process. The approach is based on the creation of two separate graphs, one for words and one for features of different types (alignment-based, orthographic, etc.). The graphs are connected through edges that link nodes in the feature graph to nodes in the word graph, the edge weights representing the importance of a particular feature for a particular word. High quality graphs are learned during training, and the proposed method outperforms experimental baselines.
机译:在这项工作中,我们提出了一种基于图的方法以无监督的方式计算单词之间的相似度,并在此过程中利用了异构特征类型。该方法基于创建两个单独的图,一个用于单词,一个用于不同类型的特征(基于对齐,正字等)。这些图通过将特征图中的节点链接到单词图中的节点的边缘相连,边缘权重表示特定特征对于特定单词的重要性。在训练过程中可以学习到高质量的图形,并且所提出的方法优于实验基准。

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