论文标题
通过从词汇资源中提取的知识来增强单词嵌入
Enhancing Word Embeddings with Knowledge Extracted from Lexical Resources
论文作者
论文摘要
在这项工作中，我们提出了一种有效的单词矢量表示语义专业化方法。为此，我们使用传统的单词嵌入方式并应用专业方法来更好地捕获单词之间的语义关系。在我们的方法中，我们利用了诸如Babelnet等丰富词汇资源的外部知识。我们还表明，我们提出的基于Wasserstein距离的对抗性神经网络提出的专业化方法，可以对两个任务的最新方法进行改进：单词相似性和对话框状态跟踪。
In this work, we present an effective method for semantic specialization of word vector representations. To this end, we use traditional word embeddings and apply specialization methods to better capture semantic relations between words. In our approach, we leverage external knowledge from rich lexical resources such as BabelNet. We also show that our proposed post-specialization method based on an adversarial neural network with the Wasserstein distance allows to gain improvements over state-of-the-art methods on two tasks: word similarity and dialog state tracking.
