Piper File List Diagrams

Clinical NLP Transformers

Cnlpt Negation

graph TD;
        A[Annotation Engine wrapper for the PbjStarter] --> B[Start 2nd Ctakes Java Pipeline]
        B --> C[Pip Pbj]
        C --> D[Start PBJ Python Pipeline]
        D --> DefaultTokenizerPipeline
        subgraph DefaultTokenizerPipeline[Text Processing]
            E[Discover Sections] --> F[Sentence Detector]
            F --> G[Identify Tokens]
        end
        DefaultTokenizerPipeline --> final
        subgraph final[Finalization]
            H[Create a dictionary lookup] --> I[Send jcas to Artemis Queue]
        end

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Degree-of and location-of relation, events,times, temporal relations, document creation time relations and core

(Default Advanced Pipeline)

graph TD;
    A[Clinical Pipeline with degree-of, location-of, events, times, temporal and event-doc creation time relations] --> B[Commands and parameters to create a default coreference processing sub-pipeline.]

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Pipeline with coreference resolution.

(Default Coref Pipeline)

graph TD;
    A[Commands and parameters to create a plaintext document processing pipeline with UMLS entity lookup. ] --> B[Commands and parameters to create a default coreference processing sub-pipeline.]

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Commands and parameters to create a plaintext document processing pipeline with UMLS entity lookup.

(Default Fast Pipeline)

graph TD;
    A[Start simple token processing] --> B[Add non-core annotators]
    B --> C[Add Chunkers]
    C --> D[Default fast dictionary lookup]
    D --> E[Add Cleartk Entity Attribute annotators]
    E --> F[Log run time stats and completion]

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Pipeline with degree-of and location-of relations and coreference resolution.

(Default Relation Coref Pipeline)

graph TD;
    A[Pipeline with degree-of and location-of relations] --> B[Coreference resolution]

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Clinical Pipeline with degree-of and location-of relations.

(Default Relation Pipeline)

graph TD;
    A[Default Clinical Pipeline] --> B[degree-of, relation-of Pipeline]

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Clinical Pipeline with degree-of, location-of, events, times, temporal and event-doc creation time relations.

(Default Relation Temporal Pipeline)

graph TD;
    A[Clinical Pipeline with degree-of and location-of relations] --> B[events, times, event-event, event-time and event-doc creation time relations]

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Pipeline with events, times, temporal relations, document creation time relations and coreferences.

(Default Temporal Coref Pipeline)

graph TD;
    A[Default Temporal pipeline] --> B[Coreference resolution]

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Clinical Pipeline with events, times, event-event and event-time relations plus event-document creation time relations.

(Default Temporal Pipeline)

graph TD;
    A[Clinical Pipeline] --> B[events, times, event-event, event-time and event-doc creation time relations]

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Commands and parameters for a small tokenization pipeline.

(Default Tokenizer Pipeline)

graph TD;
    A[Annotates Document as a single Section] --> B[Annotates Sentences based upon an OpenNLP model]
    B --> C[Annotates Document Penn TreeBank Tokens]

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Commands and parameters for a small tokenization pipeline with sections, paragraphs and lists.

(Full Tokenizer Pipeline)

graph TD;
    A[ Annotate sections by known regex ] --> B[Annotates Sentences based upon an OpenNLP model]
    B --> C[paragraphs are parsed using empty lines as separators]
    C --> D[Fix sentences so that no sentence spans across two or more paragraphs]
    D --> E[Use regular expressions created for the Pitt notes to discover formatted lists and tables]
    E --> F[Fix sentences so that no sentence spans across two or more list entries]
    F --> G[Now we can finally tokenize, tag parts of speech and chunk using adjusted sentences]

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