The invention discloses a geographical science domain
named entity recognition method, which is used for recognizing geographical science core term entities and geographical location entities. The method mainly comprises three steps of (1) establishing a geographical science domain dictionary, and using a new word discovery
algorithm to identify new words in the geographical science domain in an unsupervised way; (2) training and testing based on a
conditional random field (CRF) model and a multichannel
convolutional neural network (MCCNN) model; (3) carrying out
error correcting and fusion on entities recognized by the models by using a rule-based method. According to the geographical science domain
named entity recognition method, the new words of the domain are identified as the dictionary in an unsupervised way by using the new word discovery
algorithm, so that the work distinguishing effect is improved. The semantic vectors of the words are learnt from large-scale unmarked data in an unsupervised way, and basic characteristics of the words are synthesized and are taken as the input characteristics of the MCCNN model, so that manual selection and construction of the characteristics are avoided. The predicting results of the two models are fused by means of a custom rule, so that the problem of error marking in a recognition process can be corrected.