Patent ID: 8560477

Claim:
A computer-implemented method, comprising: creating a graph having a plurality of unique vertices in which vertices in a first set of vertices represent n-grams that are each associated with a respective part-of-speech (POS) and that were derived from labeled source domain text, and in which vertices in a different second set of vertices represent n-grams that are not associated with a POS and that were derived from unlabeled target domain text; for different pairs of vertices in the plurality of vertices in which at least one vertex in the pair is in the second set of vertices, and at least one vertex in the pair is a k-nearest neighbor of the other vertex in the pair where k is greater than 1, determining respective features of each vertex in the pair based at least partially on features of words that surround occurrences of the particular vertex's n-gram in the source or target domain text; calculating a respective measure of similarity between the vertices in each of the pairs based at least partially on a distance between the respective features of the pair, and using the measure of similarity to weight a graph edge between the pair; determining one or more respective POS probabilities for each of a plurality of unique n-grams in the unlabeled target domain text using a semi-supervised statistical model trained on unlabeled text from a target domain; smoothing the one or more determined POS probabilities using the graph, wherein smoothing the one or more determined POS probabilities for a respective n-gram is based on an edge weight between a first vertex in the similarity graph that represents the respective n-gram and a different, second vertex in the similarity graph that represents a different n-gram that is associated with a POS; and labeling one or more of the n-grams with a respective POS based on a combination of the particular n-gram's respective determined probabilities and smoothed probabilities.