3. The method as recited in claim 1 , wherein the obtaining the first word and the second word, each of the first word and the second word including at least one term, comprises: determining a threshold of an appearance frequency of the first word and the second word; and determining that the appearance frequency in each of the first word and the second word is higher than the threshold.
1. A computer implemented method for identifying synonyms, the method comprising: obtaining, by a server, a first word and a second word, each of the first word and the second word including at least one term; determining that a shortest edit distance between the first word and the second word is less than or equal to an edit distance threshold; determining whether both of the first word and the second word exist in a preset knowledge database; in response to determining at least the first word does not exist in the preset knowledge database, segmenting the first word to obtain one or more terms included in the first word; determining whether the one or more terms after segmentation exist in the preset knowledge database; and searching, in response to determining that the one or more terms after segmentation exist in the preset knowledge database, a smallest granularity type with a highest weight value for each of the one or more terms in the preset knowledge database; finding, in response to determining that both of the first word and the second word exist in the preset knowledge database, the smallest granularity type with the highest weight value for each of the first word and the second word in the preset knowledge database; and determining whether the first word and second word have a same smallest granularity type with a highest weight value including, determining that the first word and the second word are synonyms, in response to determining that the first word and the second word have the same smallest granularity type with the highest weight value; and determining that the two words are non-synonyms, in response to determining that the first word and the second word do not have the same smallest granularity type with the highest weight value.