Semantic Entanglement on Verb Negation
Semantic Entanglement on Verb Negation
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DOI：
10.5220/0010560000710078
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Yuto Kikuchi;Kazuo Hara;Ikumi Suzuki
中科院分区：
文献类型：
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作者：
Yuto Kikuchi;Kazuo Hara;Ikumi Suzuki
由Mikolov等人在2013年开发的word2vec是一种划时代的方法，它将单词嵌入到向量空间中以捕捉它们的细粒度含义。然而，word2vec的可靠性是不一致的。为了评估词向量的可靠性，我们执行Mikolov的词类比任务，其中提供了词，词和词。在词与词呈现特定关系的条件下，任务包括搜索词汇表并针对相同关系返回最相关的词。我们对100个典型的日语动词进行了一个实验，使用word2vec为动词返回否定词，并调查了上下文的影响（即，周围的话）对正确或不正确的反应。结果表明，当动词意义和否定关系纠缠在一起时，由于动词否定的语义计算不成立，任务失败。
The word2vec, developed by Mikolov et al. in 2013, is an epoch-creating method that embeds words into a vector space to capture their fine-grained meaning. However, the reliability of word2vec is inconsistent. To evaluate the reliability of word vectors, we perform Mikolov’s word analogy task, where word , word , and word are provided. Under the condition that word exhibits a particular relation with word , the task involves searching the vocabulary and returning the most relevant word for word for the same relation. We conduct an experiment to return negative words for verbs using word2vec for 100 typical Japanese verbs and investigate the effect of context (i.e., surrounding words) on correct or incorrect responses. It is shown that the task fails when the sense of verbs and negative relation are entangled because the semantic calculation of verb negation does not hold.