5. A computer-implemented method executed by one or more processors, the method comprising: receiving a training phrase; normalizing the training phrase according to one or more lexicographic normalization rules; locating the normalized training phrase in a normalized phrase table, the normalized phrase table including a plurality of key-value pairs, each key-value pair having a key that includes a normalized phrase and a value that includes one or more un-normalized phrases associated with the normalized phrase of the key and one or more parameters associated with each un-normalized phrase; associating one or more weights to one or more un-normalized phrases associated with the key-value pair for the identified normalized training phrase in the normalized phrase table based on a relation of each associated un-normalized phrase to the received training phrase; determining a degree of match between the received training phrase and a specific un-normalized phrase associated with the located normalized training phrase, the degree of match being determined according to a similarity measure, wherein associating one or more weights comprises: associating a first weight to the specific un-normalized phrase when the training phrase has a high degree of match with the specific un-normalized phrase, and associating a second weight to the specific un-normalized phrase when the training phrase has a low degree of match with the specific un-normalized phrase; and training a machine learning model using the one or more un-normalized phrases and the associated one or more weights.
5. A computer-implemented method executed by one or more processors, the method comprising: receiving a training phrase; normalizing the training phrase according to one or more lexicographic normalization rules; locating the normalized training phrase in a normalized phrase table, the normalized phrase table including a plurality of key-value pairs, each key-value pair having a key that includes a normalized phrase and a value that includes one or more un-normalized phrases associated with the normalized phrase of the key and one or more parameters associated with each un-normalized phrase; associating one or more weights to one or more un-normalized phrases associated with the key-value pair for the identified normalized training phrase in the normalized phrase table based on a relation of each associated un-normalized phrase to the received training phrase; determining a degree of match between the received training phrase and a specific un-normalized phrase associated with the located normalized training phrase, the degree of match being determined according to a similarity measure, wherein associating one or more weights comprises: associating a first weight to the specific un-normalized phrase when the training phrase has a high degree of match with the specific un-normalized phrase, and associating a second weight to the specific un-normalized phrase when the training phrase has a low degree of match with the specific un-normalized phrase; and training a machine learning model using the one or more un-normalized phrases and the associated one or more weights. 10. The method of claim 5 , where training the machine learning model includes training a language identification model.