Representing words as regions in vector space
Representing words as regions in vector space
复制标题
将单词表示为向量空间中的区域
DOI：
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复制
发表时间：
2009
期刊：
影响因子：
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通讯作者：
K. Erk
中科院分区：
文献类型：
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作者：
K. Erk
词义的向量空间模型通常将单词的含义表示为通过对所有其语料库出现进行求和而计算的向量。在空间上接近这一点的词可以被认为在意义上与它相似。但是，在这一点周围，具有类似意义的区域延伸了多远呢？在本文中，我们讨论了两个模型，表示词的意义，在向量空间中的区域。这两种表示都可以从向量空间中的传统点表示计算出来。我们发现，这两种模型在令牌分类任务上的F分数都超过95%。
Vector space models of word meaning typically represent the meaning of a word as a vector computed by summing over all its corpus occurrences. Words close to this point in space can be assumed to be similar to it in meaning. But how far around this point does the region of similar meaning extend? In this paper we discuss two models that represent word meaning as regions in vector space. Both representations can be computed from traditional point representations in vector space. We find that both models perform at over 95% F-score on a token classification task.
影响因子：
4.1
作者：
NOSOFSKY, RM
通讯作者：
NOSOFSKY, RM