Sememe Attention over Target model

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.