1. A computer implemented method, in a data processing system comprising a processor and a memory comprising instructions which are executed by the processor to cause the processor to implement a short form/long form detection system, the method comprising: ingesting a corpus comprising one or more documents; splitting the one or more documents into one or more tokens; selecting one or more tokens as one or more candidate short forms; for each candidate short form, generating one or more n-gram combinations, resulting in one or more candidate short form and n-gram combination pairs; for each candidate short form and n-gram combination pair; calculating an approximate string matching distance; calculating a best possible alignment score using a string matching scoring algorithm based on a maximum pooling over one or more possible alignments between the candidate short form and n-gram combination; evaluating the n-gram combination to determine if the approximate string matching distance meets a first pre-determined threshold or the best possible alignment score meets a second pre-determined threshold; if the n-gram combination meets either the first pre-determined threshold or the second pre-determined threshold, then; calculating a confidence score based on the approximate string matching distance; calculating a topic similarity score; and calculating a semantic similarity score; determining the validity, through a meta learner, of each candidate short form and n-gram combination pair based upon each short form and n-gram combination pair's confidence score, topic similarity score, and semantic similarity score; and if valid, storing the short form and n-gram combination pair in a repository.
1. A computer implemented method, in a data processing system comprising a processor and a memory comprising instructions which are executed by the processor to cause the processor to implement a short form/long form detection system, the method comprising: ingesting a corpus comprising one or more documents; splitting the one or more documents into one or more tokens; selecting one or more tokens as one or more candidate short forms; for each candidate short form, generating one or more n-gram combinations, resulting in one or more candidate short form and n-gram combination pairs; for each candidate short form and n-gram combination pair; calculating an approximate string matching distance; calculating a best possible alignment score using a string matching scoring algorithm based on a maximum pooling over one or more possible alignments between the candidate short form and n-gram combination; evaluating the n-gram combination to determine if the approximate string matching distance meets a first pre-determined threshold or the best possible alignment score meets a second pre-determined threshold; if the n-gram combination meets either the first pre-determined threshold or the second pre-determined threshold, then; calculating a confidence score based on the approximate string matching distance; calculating a topic similarity score; and calculating a semantic similarity score; determining the validity, through a meta learner, of each candidate short form and n-gram combination pair based upon each short form and n-gram combination pair's confidence score, topic similarity score, and semantic similarity score; and if valid, storing the short form and n-gram combination pair in a repository. 5. The method as recited in claim 1 , further comprising: calculating the confidence score based on a number of times the short form and n-gram combination pair is found within the corpus.