// Define some global attributes
include::_globattr.adoc[]

Clinical documents pipeline
~~~~~~~~~~~~~~~~~~~~~~~~~~~
This project is the top-level, main project for processing a clinical
document through the entire {osp-short} pipeline, including sentence
detection, <<cd_pos_tagger,part of speech tagging>>,
<<cd_chunker,chunking>>, named entity recognition,
xref:cd_necontexts[context detection, and negation detection].

The pipeline can process two types of documents

- plain text files
- Clinical Document Architecture (CDA) XML files that conform to the DTD provided


Analysis engines (annotators)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
- *AggregateCdaProcessor.xml* for CDA documents conforming to the provided DTD
+
--
The file +desc/analysis_engine/AggregateCdaProcessor.xml+ is the
aggregate analysis engine to use to run the entire pipeline, including
the CdaCasInitialzer analysis engine, which reads CDA documents that
conform to the DTD provided, and create Segment annotations based on
the sections within the CDA document.

*Parameters*::
  ChunkerCreatorClass;; the full class name of an implementation of the interface edu.mayo.bmi.uima.chunker.ChunkerCreator
--
+
- *AggregatePlaintextProcessor.xml* for plain text documents
+
--
The file +desc/analysis_engine/AggregatePlaintextProcessor.xml+ is the
aggregate analysis engine to use to run the entire pipeline, including
the SimpleSegmentAnnotator analysis engine, which creates a Segment
annotation that wraps the entire plain text document. Other annotators
in the pipeline require at least one Segment annotation.

*Parameters*::
  SegmentID;; the identifier or name to assign to the Segment annotation
  ChunkerCreatorClass;; the full class name of an implementation of the interface edu.mayo.bmi.uima.chunker.ChunkerCreator
--

NOTE: The ChunkCreatorClass parameter of both annotators is set to
edu.mayo.bmi.uima.chunker.PhraseTypeChunkCreator so that each phrase
type gets its own type of annotation, rather than having all chunks be
of type Chunk.
