用图像和词典定义生成不依赖上下文的词向量
Using Images to Find Context-Independent Word Representations in Vector Space
- 通过词义图像自编码器提取语义表示
- 在词相似度等任务上表现接近上下文方法
- 训练速度快，适合快速构建词向量
现有许多词向量方法依赖文本上下文捕捉语义关系。本文提出一种新方法，利用词典定义和图像表征，构建独立于上下文的词向量。通过在词图像上使用自编码器学习有意义的表示，并据此计算词向量。我们在词相似性、概念分类和异常检测任务上进行评估，结果表明该方法性能与基于上下文的方法相当，但训练时间显著更短。
原文摘要 · Abstract (English)
Many methods have been proposed to find vector representation for words, but most rely on capturing context from the text to find semantic relationships between these vectors. We propose a novel method of using dictionary meanings and image depictions to find word vectors independent of any context. We use auto-encoder on the word images to find meaningful representations and use them to calculate the word vectors. We finally evaluate our method on word similarity, concept categorization and outlier detection tasks. Our method performs comparably to context-based methods while taking much less training time.
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