A News Name Correction Method Based on Context Semantics

In news texts, incorrect fields in names will affect or even change the semantic expression of the text and the particularity of name fields will generate duplicate name or ambiguity. For solving these problems, this paper proposes a novel news name correction method based on context semantics. This method uses convolutional neural network to extract the semantic information of texts, and adopts word activation model to calculate the degree of association between other words and name fields in texts to capture and use the semantic information of text context. At the same time, aiming at the problem of low recognition caused by errors in the field of human name in texts, the entity boundary recognition algorithm of names is used to improve the recognition and extraction effect of names that are suspected to contain errors in the text. The experimental results show that the method can effectively identify the names in the text and correct the errors.