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首页> 外文期刊>Physica, A. Statistical mechanics and its applications >Word sense induction using word embeddings and community detection in complex networks
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Word sense induction using word embeddings and community detection in complex networks

机译：在复杂网络中使用Word Embeddings和社区检测的单词感应

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Word Sense Induction (WSI) is the ability to automatically induce word senses from corpora. The WSI task was first proposed to overcome the limitations of manually annotated corpus that are required in word sense disambiguation systems. Even though several works have been proposed to induce word senses, existing systems are still very limited in the sense that they make use of structured, domain-specific knowledge sources. In this paper, we devise a method that leverages recent findings in word embeddings research to generate context embeddings, which are embeddings containing information about the semantical context of a word. In order to induce senses, we modeled the set of ambiguous words as a complex network. In the generated network, two instances (nodes) are connected if the respective context embeddings are similar. Upon using well-established community detection methods to cluster the obtained context embeddings, we found that the proposed method yields excellent performance for the WSI task. Our method outperformed competing algorithms and baselines, in a completely unsupervised manner and without the need of any additional structured knowledge source. (C) 2019 Elsevier B.V. All rights reserved.
机译：词感应诱导（WSI）是能够自动诱导来自Corpora的词语感官。首先提出了WSI任务以克服词义歧义系统所需的手动注释语料库的限制。尽管已经提出了几种作品来诱导字感，但现有系统仍然非常有限，因为它们利用结构化的域特定知识来源。在本文中，我们设计了一种方法，该方法利用Word Embeddings Researd中的最近发现来生成上下文嵌入，这是包含有关单词语义上下文的信息的嵌入。为了诱导感官，我们将一组模糊的单词建模为复杂网络。在所生成的网络中，如果相应的上下文嵌入式相似，则连接两个实例（节点）。在使用良好的群落检测方法来聚类所获得的上下文嵌入时，我们发现该方法为WSI任务产生了出色的性能。我们的方法以完全无监督的方式表现优于竞争算法和基线，而无需任何额外的结构化知识来源。 （c）2019 Elsevier B.v.保留所有权利。

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