--- Parsers ---
    -- SpaCy --
        |- Philosophy -
            Fast, easy to use, Industrial Strength
        |- Models -
            Pre-trained, "en_core_web_trf", "en_core_web_sm/md/lg"
        |- Outputs - 
            Provides universal dependancies (UD) labels by default
        |- Use -
            Very easy to use, a few lines of code to parse sentence and its dependencies
        |- Integration -
            Works very well with python data science stack, networkx integrates easily
        |- Performance -
            Larger models very accurate, smaller are very fast

    -- Stanford Stanza --
        |- Philosophy -
            Pure python + modern version of Stanford Core NLP
            Research oriented, highly accurate
        |- Models - 
            Pre-trained models on different treebanks can handle complex gramatical sctructures.
        |- Output -
            Universal dependencies
        |- Use -
            Good API, is clean and fits well with python
        |- Integration - 
            Pure python integrated well with oher python libraries
        |- Performance -
            Accuracy is among the best available, Speed is slower than spacy non-transformer models
    
    -- Allen NLP --
        |- Philosophy -
            Research first, built on python
            Designed for "state of the art" deep learning models in NLP "Go to choice if you plan to modify or train your own models"
        |- Models -
            Biffane dependancy parser is most widely used (highly accurate)
        |- Output -
            Universal Dependancies
        |- Use -
            More difficult than SpaCy or Stanza, requires better understanding of the libraries abstactions
        |- Integration -
            Excellent in the python ecosystem, for pre-trained model is overkill
        |- Performance -
            State-of-the-art accuracy, inference speed can be slower due to model complexity
    
    -- Spark NLP ---
        |- Philosophy -
            Built on Apache Spark or scalable, distributed NLP processing
            For massive datasets in a distributed computing
        |- Models -
            Provided its own anotated models
            often transformer architecture
        |- Output -
            Universal Dependancies
        |- Use -
            Good if familiar with spark ML API
            Setup more involved than pure python libraries
        |- Integration -
            Ideal for big data pipelines
            Unnesisarily heavy for single corpus analysis
        |- Performance -
            Very high accuracy
            Designed for speed and scale on clusters
    
    -- Overall --
        |- SpaCy or Stanze --
            SpaCy is much simpler to set up and use, - robust, highly accurate system to be set up quickly and relativly simply
            Stanza is more complex and requires more complex setup, - maximise baseline accuracy when parsing in exchange for speed and simlicity

    -- Choice --
        |- SpaCy -
            Use SpaCy initially, if parsing errors appear will switch to Stanza to check issues