一种基于全局领域和短期记忆因子的图模型

词义消歧是一项具有挑战性的自然语言处理难题。作为词义消歧中的一种优秀的半监督消歧算法，遗传蚁群词义消歧算法能快速进行全文词义消歧。该算法采用了一种局部上下文的图模型来表示语义关系，以此进行词义消歧。然而，在消歧过程中却丢失了全局语义信息，出现了消歧结果冲突的问题，导致算法精度降低。因此，提出了一种基于全局领域和短期记忆因子改进的图模型来表示语义以解决这个问题。该图模型引入了全局领域信息，增强了图对全局语义信息的处理能力。同时根据人的短期记忆原理，在模型中引入了短期记忆因子，增强了语义间的线性关系，避免了消歧结果冲突对词义消歧的影响。大量实验结果表明：与经典词义消歧算法相比，所提的改进图模型提高了词义消歧的精度。

A graph model based on global domainand short-term memory factor

Word sense disambiguation (WSD) is a challenging problem in natural language processing. As an excellent semi-supervised disambiguation algorithm in WSD, the genetic max-minant system word sense disambiguation (GMMSWSD) can perform full-text WSD quickly. The algorithm uses a graph based on local context to represent semantic relationships for word sense disambiguation. However, in the process of disambiguation, global semantic information is lost and inconsistent disambiguation results occur, which leads to lower accuracy of the algorithm. We therefore propose an improved graph model based on global domain and short-term memory factor to solve the abovementioned problems. The new graph model introduces global domain information to enhance the processing ability of global semantic information. At the same time, according to the principle of short-term memory, we introduce the short-term memory factor into the model, which can enhance the linear relationship between semantics and avoid the influence of inconsistent disambiguation results on word sense disambiguation. Experimental results show that compared with the classical word sense disambiguation algorithm, the proposal's precision of word sense disambiguation is improved.