1. A computer-implemented method comprising: labeling text items in a first set of text, in a first language, from a multi-lingual digital document corpus, with first labels indicating first named entity classes by inheriting respective class labels of corresponding documents in the corpus into anchors in the first set of text which hyperlink the corresponding documents; generating respective confidence scores for the text items in the first set of text labeled with the named entity classes, the confidence scores including weights based on features of the labeled text items and transition probabilities among respective ones of the labeled text items; applying heuristic rules to remove labels having confidence scores lower than the confidence scores of other labeled text items from the first set of text; automatically labeling text items in a second set of text of a second language from the multi-lingual digital document corpus, from parallel sentences in the first set of text, the text items in the second set of text being labeled with second labels indicating second named entity classes, the parallel sentences being pairs of sentences with a same semantic meaning in the first and second languages; selecting a subset of the parallel sentences including labeled text from the first and second sets of text, wherein, for the selected parallel sentences, the text items having disagreeing labels with lower confidence scores than the text items having agreeing labels and wherein the subset comprises fewer than a total number of the parallel sentences of the first set of labeled text and the second set of labeled text; using the selected subset of the parallel sentences to train a machine learning component; and using the trained machine learning component to label a third set of text, in a language of the multi-lingual document corpus, with one or more of the first labels or the second labels.