8. A system for text processing, comprising: one or more processors configured to: train a classifier for classifying text, wherein: the training is based on a plurality of training sample entries; a training sample entry in the plurality of training sample entries includes: a character count; an independent use rate; a phrase structure rule value indicating whether the training sample entry complies with phrase structure rules; a semantic attribute value indicating an inclusion state of the training sample entry in a predetermined set of enumerated entries; an overlap attribute value indicating overlap of the training sample entry with another entry in the predetermined set of enumerated entries; and a classification result indicating whether the training sample entry is a compound semantic unit or a smallest semantic unit; and build a lexicon of smallest semantic units, comprising: receiving an entry to be classified; using the trained classifier to determine whether the entry to be classified is a smallest semantic unit or a compound semantic unit; and in the event that the entry is determined to be a smallest semantic unit, adding the entry to the lexicon of smallest semantic units; segment received text based on the lexicon of smallest semantic units to obtain medium-grained segmentation results; merge the medium-grained segmentation results to obtain coarse-grained segmentation results, the coarse-grained segmentation results having coarser granularity than the medium-grained segmentation results; look up in the lexicon of smallest semantic units respective search elements that correspond to segments in the medium-grained segmentation results; and form fine-grained segmentation results based on the respective search elements, the fine-grained segmentation results having finer granularity than the medium-grained segmentation results; and one or more memories coupled to the one or more processors, configured to provide the one or more processors with instructions.
8. A system for text processing, comprising: one or more processors configured to: train a classifier for classifying text, wherein: the training is based on a plurality of training sample entries; a training sample entry in the plurality of training sample entries includes: a character count; an independent use rate; a phrase structure rule value indicating whether the training sample entry complies with phrase structure rules; a semantic attribute value indicating an inclusion state of the training sample entry in a predetermined set of enumerated entries; an overlap attribute value indicating overlap of the training sample entry with another entry in the predetermined set of enumerated entries; and a classification result indicating whether the training sample entry is a compound semantic unit or a smallest semantic unit; and build a lexicon of smallest semantic units, comprising: receiving an entry to be classified; using the trained classifier to determine whether the entry to be classified is a smallest semantic unit or a compound semantic unit; and in the event that the entry is determined to be a smallest semantic unit, adding the entry to the lexicon of smallest semantic units; segment received text based on the lexicon of smallest semantic units to obtain medium-grained segmentation results; merge the medium-grained segmentation results to obtain coarse-grained segmentation results, the coarse-grained segmentation results having coarser granularity than the medium-grained segmentation results; look up in the lexicon of smallest semantic units respective search elements that correspond to segments in the medium-grained segmentation results; and form fine-grained segmentation results based on the respective search elements, the fine-grained segmentation results having finer granularity than the medium-grained segmentation results; and one or more memories coupled to the one or more processors, configured to provide the one or more processors with instructions. 14. The system of claim 8 , wherein segmenting received text based on the lexicon of smallest semantic units to obtain medium-grained segmentation results includes resolving an ambiguity of the medium-grained segmentation results.