1. A method, in a data processing system, for identifying nonsense passages in documents being ingested into a corpus, the method comprising: receiving, by a natural language processing pipeline configured to execute in the data processing system, an input document to be ingested into a corpus; dividing, by the natural language processing pipeline, the input document into a plurality of input passages; identifying, by a filter component of the natural language processing pipeline, whether each input passage is a nonsense passage based on a value of a metric determined according to a set of feature counts, wherein identifying whether a given input passage is a nonsense passage comprises: annotating, by an annotator in the natural language processing pipeline, the given input passage within the plurality of input passages with linguistic features to form an annotated passage; counting, by metric counters component in the natural language processing pipeline, a number of instances of each type of linguistic feature in the annotated passage to form a set of feature counts; determining, by the metric counters component of the natural language processing pipeline, a value for a metric based on the set of feature counts; and comparing, by a comparator component of the natural language processing pipeline, the value for the metric to a predetermined model threshold; filtering, by the natural language processing pipeline, each input passage in the plurality of input passages based on whether the input passage is identified as a nonsense passage or not identified as a nonsense passage to form a filtered plurality of input passages; and adding, by the natural language processing pipeline, the filtered plurality of input passages into the corpus.
5. The method of claim 1 , wherein the metric comprises a ratio of a number of instances of a first pert-of-speech to a number of instances of a second part-of-speech in the input passage.