【摘要】 文本的表示是自然语言处理领域重要的工作,如何更好地表示文本语义是自然语言处理领域实际应用的重要基石。通过训练得到的词嵌入向量表示,可以认为其即代表单词本身及其含义。单词在不同语境下的含义不一样,不同的语境下涉及到一词多义问题,如何准确表示词嵌入向量以适应不同的语境也是目前研究的热点。目前词嵌入技术还处于初级阶段,还有很多问题值得深入研究。更多还原

【Abstract】 Text representation is an important work in the field of natural language processing. How to better represent text semantics is an important cornerstone of practical application in the field of natural language processing. Through the training of word embedding vector representation, we can think that it represents the word itself and its meaning. The meanings of words are different in different contexts, and different contexts involve polysemy. How to accurately represent word embedding vector to adapt to different contexts is also a hot topic of current research. At present, word embedding technology is still in its infancy, and there are still many problems worthy of further study.更多还原