1. A method of target word recognition, comprising: obtaining a candidate word set and corresponding characteristic computation data, the candidate word set comprising text data, and characteristic computation data being associated with the candidate word set; performing segmentation of the characteristic computation data to generate a plurality of text segments; combining the plurality of text segments to form a text data combination set; determining an intersection of the candidate word set and the text data combination set, the intersection comprising a plurality of text data combinations; determining a plurality of designated characteristic values for the plurality of text data combinations; determining, using a processor, a criterion, including: obtaining a training sample word set and sample characteristic computation data, the sample characteristic computation data comprising a plurality of sample words and designated characteristic values of the plurality of sample words; obtaining a sample text data combination set based on the plurality of sample words; determining a plurality of designated characteristic values of sample text data combinations in an intersection of the sample text data combination set and the training sample word set; and setting a threshold value of a designated characteristic value of a sample text data combination in the intersection as a part of the criterion; and based at least in part on the plurality of designated characteristic values for the plurality of text data combinations and according to at least the criterion, recognizing among the plurality of text data combinations, target words whose characteristic values fulfill the criterion.
1. A method of target word recognition, comprising: obtaining a candidate word set and corresponding characteristic computation data, the candidate word set comprising text data, and characteristic computation data being associated with the candidate word set; performing segmentation of the characteristic computation data to generate a plurality of text segments; combining the plurality of text segments to form a text data combination set; determining an intersection of the candidate word set and the text data combination set, the intersection comprising a plurality of text data combinations; determining a plurality of designated characteristic values for the plurality of text data combinations; determining, using a processor, a criterion, including: obtaining a training sample word set and sample characteristic computation data, the sample characteristic computation data comprising a plurality of sample words and designated characteristic values of the plurality of sample words; obtaining a sample text data combination set based on the plurality of sample words; determining a plurality of designated characteristic values of sample text data combinations in an intersection of the sample text data combination set and the training sample word set; and setting a threshold value of a designated characteristic value of a sample text data combination in the intersection as a part of the criterion; and based at least in part on the plurality of designated characteristic values for the plurality of text data combinations and according to at least the criterion, recognizing among the plurality of text data combinations, target words whose characteristic values fulfill the criterion. 9. The method of claim 1 , wherein combining the plurality of sample segments includes applying an n-gram language model to the plurality of sample segments.