文本向量化原子能力

将文本表示成低维,稠密,实数向量的一种方法.基于神经网络的文本向量化成为自然语言处理领域的研究热点,尤其是对单词的向量化研究.单词作为自然语言的最基本语义单位,基于10000号对话文本数据，利用分词技术和深度学习无监督方法训练模型，实现将通信领域的文本/句子转化向量的能力。A method for representing text as low-dimensional, dense, real-valued vectors. Neural network-based text vectorization, particularly research on word vectorization, has become a prominent research hotspot in the field of natural language processing. As the most basic semantic unit of natural language, models are trained via word segmentation technology and unsupervised deep learning methods based on 10,000 dialog text samples, enabling the conversion of texts or sentences in the communication field into vectors.