1. A method for generating a natural language resource using a parallel corpus, the method comprising: utilizing at least one processor to execute computer code that performs the steps of: receiving, from a parallel corpus, natural language text in a source language and a corresponding translation of the natural language text in a target language, wherein the natural language text in the source language comprises linguistic annotations; projecting the linguistic annotations from the source language natural language text to the target language natural language text; applying one or more filters to remove at least one projected linguistic annotation from the target language natural language text that results in at least one error, wherein the one or more filters are selected from the group consisting of: a filter for correcting a translation shift error and a reattachment heuristic filter; identifying and selecting at least one target language natural language text from a plurality of target natural language texts comprising filtered projected linguistic annotations, wherein the identifying and selecting comprises identifying and selecting comprises identifying a target language natural language text having complete linguistic annotations after the one or more filters have been applied; training a machine learning model using the identified and selected at least one target language natural language text and the projected and filtered annotations of the identified and selected at least one target language natural language text; adding, using the trained machine learning model, linguistic annotations to at least one target language natural language text having incomplete linguistic annotations; and generating, using the trained machine learning model, a natural language resource comprising the target language; and training a natural language annotator using the natural language resource.