Abstract:

The new words identification becomes an issue we have to face to with the generating flood of new words on Internet. Through analysis of the appearance characteristics of the new words, candidate strings were extracted according to co-current frequency and timeliness rules of the scattered single characters. Thereafter an improved algorithm of association rules was proposed for identifying the new words from the candidate strings based on the new word characteristics—adjacency, sequence, and frequency. Experiments show that the new words can be distinguished from the general single-character strings, the rigidity of the traditional n-gram model matching is lessened, and the short-word-included-in-long-word problem is solved, meanwhile, the precision of the new words identification is increased.

Key words: new words identification, association rules, timeliness function, segmentation fragment