1. A computer-implemented method, comprising: obtaining, at a computing device having one or more processors, (i) an aligned bi-text for a source language and a target language, the aligned bi-text comprising a plurality of source-target sentence pairs, and (ii) a supervised sequence model for the source language; labeling, at the computing device, each word of a source side of the aligned bi-text using the supervised sequence model to obtain a labeled source side of the aligned bi-text; projecting, at the computing device, labels from the labeled source side to a target side of the aligned bi-text to obtain a labeled target side of the aligned bi-text, wherein each label of the labeled source and target sides of the aligned bi-text is a named entity type tag for a particular word; filtering, at the computing device, the labeled target side of the aligned bi-text for the target language to obtain a filtered target side of the aligned bi-text for training a sequence model for the target language for a named entity segmentation system, wherein the filtering comprises discarding any particular source-target sentence pair when (i) a threshold amount of tokens of the particular source-target sentence pair are unaligned or (ii) a source named entity of the particular source-target sentence pair is not aligned with a target sentence token; training, at the computing device, the sequence model for the target language using posterior regularization with soft constraints on the filtered target side to learn a set of parameters for the target language; obtaining, at the computing device, a trained sequence model for the target language using the set of parameters for the target language, the trained sequence model being configured to model a probability distribution over possible labels for text in the target language; receiving, at the computing device, an input text in the target language; analyzing, at the computing device, the input text using the trained sequence model for the target language; and generating, at the computing device, an output based on the analyzing of the input text using the trained sequence model.
1. A computer-implemented method, comprising: obtaining, at a computing device having one or more processors, (i) an aligned bi-text for a source language and a target language, the aligned bi-text comprising a plurality of source-target sentence pairs, and (ii) a supervised sequence model for the source language; labeling, at the computing device, each word of a source side of the aligned bi-text using the supervised sequence model to obtain a labeled source side of the aligned bi-text; projecting, at the computing device, labels from the labeled source side to a target side of the aligned bi-text to obtain a labeled target side of the aligned bi-text, wherein each label of the labeled source and target sides of the aligned bi-text is a named entity type tag for a particular word; filtering, at the computing device, the labeled target side of the aligned bi-text for the target language to obtain a filtered target side of the aligned bi-text for training a sequence model for the target language for a named entity segmentation system, wherein the filtering comprises discarding any particular source-target sentence pair when (i) a threshold amount of tokens of the particular source-target sentence pair are unaligned or (ii) a source named entity of the particular source-target sentence pair is not aligned with a target sentence token; training, at the computing device, the sequence model for the target language using posterior regularization with soft constraints on the filtered target side to learn a set of parameters for the target language; obtaining, at the computing device, a trained sequence model for the target language using the set of parameters for the target language, the trained sequence model being configured to model a probability distribution over possible labels for text in the target language; receiving, at the computing device, an input text in the target language; analyzing, at the computing device, the input text using the trained sequence model for the target language; and generating, at the computing device, an output based on the analyzing of the input text using the trained sequence model. 7. The computer-implemented method of claim 1 , wherein the source language is a resource-rich language having greater than an amount of labeled training data required to train the supervised sequence model for the source language, and wherein the target language is a resource-poor language less than an amount of labeled training data required to train the sequence model for the target language.