首页Sememe Attention over Target model

Sememe Attention over Target model

时间: 2024-05-30 11:15:55 浏览: 49

基于HowNet的词相似度计算

词相似度计算有重要应用在许多领域，例如自然语言处理，情报信息检索，文档聚类，自动应答，词义歧义消除，机器翻译等。本文对基于词的相似度计算进行了深入研究知网和单词相似度的计算分为三个步骤：（1）计算词素相似度，（2）计算概念相似度使用权重相似度的加权和方法，（3）取概念的最大相似度是单词相似度。本文主要介绍对音素的编号，并介绍更多分解概念描述类型的准确方法。这实验表明，提出了词相似度算法本文简单可行，计算结果为与意识相对一致。

The Sememe Attention over Target model is a natural language processing model that aims to improve the accuracy of word sense disambiguation. It does so by incorporating sememe information into the attention mechanism of the neural network. Sememes are the smallest units of meaning in a language, and they can be used to represent the various senses of a word. For example, the word "run" can have multiple senses, such as "to move quickly on foot" or "to manage or operate." These different senses can be represented by different sememes, such as "motion" and "control." The Sememe Attention over Target model uses sememe information to improve the attention mechanism of the neural network. In traditional attention models, the attention weights are calculated based on the similarity between the target word and the context words. In the Sememe Attention over Target model, the attention weights are also influenced by the sememes of the target word and the context words. By incorporating sememe information into the attention mechanism, the Sememe Attention over Target model can better distinguish between the different senses of a word, leading to more accurate word sense disambiguation. This can be useful in a variety of natural language processing tasks, such as machine translation, sentiment analysis, and text classification.

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