1. A method, comprising: at a device having one or more processors and memory: receiving input data to be converted into a symbolic representation of the input data in a target symbolic system, the symbolic representation comprising a set of characters in the target symbolic system; identifying a first candidate character for the symbolic representation based on a first portion of the input data, and a second candidate character for the symbolic representation based on a second portion of the input data, wherein the first candidate character has at least a first pronunciation and a second pronunciation each applicable to a respective usage context; generating a plurality of candidate character strings, including at least a first candidate string comprising at least the first candidate character and the second candidate character; and converting the input data to a selected one of the plurality of candidate character strings, said converting comprising: determining a respective probability that the first candidate character string is a correct symbolic representation of the input data using a language model that individually accounts for a respective usage probability of the first candidate character in a first usage context comprising the second candidate character in combination with the first pronunciation of the first candidate character, and not the second pronunciation of the first candidate character, and wherein the language model is trained on an annotated corpus that associates the first pronunciation with the first candidate character used in respective contexts comprising the second candidate character.
1. A method, comprising: at a device having one or more processors and memory: receiving input data to be converted into a symbolic representation of the input data in a target symbolic system, the symbolic representation comprising a set of characters in the target symbolic system; identifying a first candidate character for the symbolic representation based on a first portion of the input data, and a second candidate character for the symbolic representation based on a second portion of the input data, wherein the first candidate character has at least a first pronunciation and a second pronunciation each applicable to a respective usage context; generating a plurality of candidate character strings, including at least a first candidate string comprising at least the first candidate character and the second candidate character; and converting the input data to a selected one of the plurality of candidate character strings, said converting comprising: determining a respective probability that the first candidate character string is a correct symbolic representation of the input data using a language model that individually accounts for a respective usage probability of the first candidate character in a first usage context comprising the second candidate character in combination with the first pronunciation of the first candidate character, and not the second pronunciation of the first candidate character, and wherein the language model is trained on an annotated corpus that associates the first pronunciation with the first candidate character used in respective contexts comprising the second candidate character. 7. The method of claim 1 , further comprising: receiving one or more manual input of annotations to a subset of text associated with a corpus, wherein a manual input of annotation indicates for an instance of a heteronymous character an appropriate pronunciation of that heteronymous character based at least in part on a context associated with the instance, wherein an annotation is associated with a symbol associated with that heteronymous character; and automatically annotating at least a portion of the text associated with the corpus that has not been manually annotated based at least in part on the received one or more manual input of annotations.