NASARI: a Novel Approach to a Semantically-Aware Representation of Items
NASARI: a Novel Approach to a Semantically-Aware Representation of Items
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DOI：
10.3115/v1/n15-1059
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发表时间：
2015
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影响因子：
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通讯作者：
José Camacho-Collados;Mohammad Taher Pilehvar;Roberto Navigli
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作者：
José Camacho-Collados;Mohammad Taher Pilehvar;Roberto Navigli
单个词义和概念的语义表示对于自然语言处理中的一些应用至关重要。到目前为止，概念建模技术主要是基于词典资源（如WordNet）或百科资源（如Wikipedia）的表示。我们提出了一种矢量表示技术，结合这两种类型的资源的互补知识。由于其使用明确的语义结合了一种新的基于聚类的降维和有效的加权方案，我们的表示在两个标准基准中的多个数据集上达到了最先进的性能：词相似性和意义聚类。我们将在http://lcl.uniroma1.it/nasari/上发布我们的矢量表示。
The semantic representation of individual word senses and concepts is of fundamental importance to several applications in Natural Language Processing. To date, concept modeling techniques have in the main based their representation either on lexicographic resources, such as WordNet, or on encyclopedic resources, such as Wikipedia. We propose a vector representation technique that combines the complementary knowledge of both these types of resource. Thanks to its use of explicit semantics combined with a novel cluster-based dimensionality reduction and an effective weighting scheme, our representation attains state-of-the-art performance on multiple datasets in two standard benchmarks: word similarity and sense clustering. We are releasing our vector representations at http://lcl.uniroma1.it/nasari/.