9. A computing device comprising: a non-transitory computer-readable medium having a set of instructions stored thereon; and one or more processors configured execute the set of instructions, which causes the computing device to perform operations comprising: obtaining (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 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 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 named entity type tag for a particular word; filtering the labeled target side 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) the particular source-target sentence pair comprises a named entity having a confidence level less than a confidence threshold or (ii) the particular source-target sentence pair comprises no named entities; training 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; using the set of parameters for the target language, obtaining a trained sequence model 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 an input text in the target language; analyzing the input text using the trained sequence model for the target language; and generating an output based on the analyzing of the input text using the trained sequence model.
9. A computing device comprising: a non-transitory computer-readable medium having a set of instructions stored thereon; and one or more processors configured execute the set of instructions, which causes the computing device to perform operations comprising: obtaining (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 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 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 named entity type tag for a particular word; filtering the labeled target side 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) the particular source-target sentence pair comprises a named entity having a confidence level less than a confidence threshold or (ii) the particular source-target sentence pair comprises no named entities; training 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; using the set of parameters for the target language, obtaining a trained sequence model 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 an input text in the target language; analyzing the input text using the trained sequence model for the target language; and generating an output based on the analyzing of the input text using the trained sequence model. 11. The computing device of claim 9 , 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 having less than an amount of labeled training data required to train the sequence model for the target language.