Constituency TreeBank

    A treebank is a corpus where the sentences in each language are syntactically (if necessary morphologically) annotated. In the treebanks, the syntactic annotation usually follows constituent and/or dependency structure.
    
    Treebanks annotated for the syntactic or semantic structures of the sentences are essential for developing state-of-the-art statistical natural language processing (NLP) systems including part-of-speech-taggers, syntactic parsers, and machine translation systems. There are two main groups of syntactic treebanks, namely treebanks annotated for constituency (phrase structure) and the ones that are annotated for dependency structure.
    
    ## Data Format
    
    We extend the original format with the relevant information, given between curly braces. For example, the word 'problem' in a sentence in the standard Penn Treebank notation, may be represented in the data format provided below:
    
    	(NN problem)
    
    After all levels of processing are finished, the data structure stored for the same word has the following form in the system.
    
    	(NN {turkish=sorunu} {english=problem} 
    	{morphologicalAnalysis=sorun+NOUN+A3SG+PNON+ACC}
    	{metaMorphemes=sorun+yH}
    	{semantics=TUR10-0703650})
    
    As is self-explanatory, 'turkish' tag shows the original Turkish word; 'morphologicalanalysis' tag shows the correct morphological parse of that word; 'semantics' tag shows the ID of the correct sense of that word; 'namedEntity' tag shows the named entity tag of that word; 'propbank' tag shows the semantic role of that word for the verb synset id (frame id in the frame file) which is also given in that tag.
    
    Video Lectures
    ============
    
    [<img src=https://github.com/StarlangSoftware/AnnotatedTree/blob/master/video1.jpg width="50%">](https://youtu.be/LfMf1bo3tEw)[<img src=https://github.com/StarlangSoftware/AnnotatedTree/blob/master/video2.jpg width="50%">](https://youtu.be/QoFPb9XY8Vc)
    
    For Developers
    ============
    You can also see [Python](https://github.com/starlangsoftware/AnnotatedTree-Py), [Java](https://github.com/starlangsoftware/AnnotatedTree), [C++](https://github.com/starlangsoftware/AnnotatedTree-CPP), [C](https://github.com/starlangsoftware/AnnotatedTree-C), [Js](https://github.com/starlangsoftware/AnnotatedTree-Js), [Swift](https://github.com/starlangsoftware/AnnotatedTree-Swift), or [C#](https://github.com/starlangsoftware/AnnotatedTree-CS) repository.
    
    ## Requirements
    
    * [Python 3.7 or higher](#python)
    * [Maven](#maven)
    * [Git](#git)
    
    ### Python 
    
    To check if you have a compatible version of Python installed, use the following command:
    
        python -V
        
    You can find the latest version of Python [here](https://www.python.org/downloads/).
    
    ### Git
    
    Install the [latest version of Git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git).
    
    ## Pip Install
    
    	pip3 install NlpToolkit-AnnotatedTree-Cy
    
    ## Download Code
    
    In order to work on code, create a fork from GitHub page. 
    Use Git for cloning the code to your local or below line for Ubuntu:
    
    	git clone <your-fork-git-link>
    
    A directory called DataStructure will be created. Or you can use below link for exploring the code:
    
    	git clone https://github.com/starlangsoftware/AnnotatedTree-Cy.git
    
    ## Open project with Pycharm IDE
    
    Steps for opening the cloned project:
    
    * Start IDE
    * Select **File | Open** from main menu
    * Choose `AnnotatedTree-Cy` file
    * Select open as project option
    * Couple of seconds, dependencies will be downloaded. 
    
    Detailed Description
    ============
    
    + [TreeBankDrawable](#treebankdrawable)
    + [ParseTreeDrawable](#parsetreedrawable)
    + [LayerInfo](#layerinfo)
    
    ## TreeBankDrawable
    
    To load an annotated TreeBank:
    
    	TreeBankDrawable(folder: str, String pattern: str)
    	a = TreeBankDrawable("/Turkish-Phrase", ".train")
    
    	TreeBankDrawable(folder: str)
    	a = new TreeBankDrawable("/Turkish-Phrase")
    
    To access all the trees in a TreeBankDrawable:
    
    	for i in range(a.sentenceCount()):
    		parseTree = a.get(i);
    		....
    	}
    
    ## ParseTreeDrawable
    
    To load a saved ParseTreeDrawable:
    
    	ParseTreeDrawable(file: str)
    	
    is used. Usually it is more useful to load TreeBankDrawable as explained above than to load ParseTree one by one.
    
    To find the node number of a ParseTreeDrawable:
    
    	nodeCount() -> int
    	
    the leaf number of a ParseTreeDrawable:
    
    	leafCount() -> int
    	
    the word count in a ParseTreeDrawable:
    
    	wordCount(excludeStopWords: bool) -> int
    	
    above methods can be used.
    
    ## LayerInfo
    
    Information of an annotated word is kept in LayerInfo class. To access the morphological analysis
    of the annotated word:
    
    	getMorphologicalParseAt(index: int) -> MorphologicalParse
    
    meaning of an annotated word:
    
    	getSemanticAt(self, index: int) -> str
    
    the shallow parse tag (e.g., subject, indirect object etc.) of annotated word: 
    
    	getShallowParseAt(self, index: int) -> str
    
    the argument tag of the annotated word:
    
    	getArgumentAt(self, index: int) -> Argument
    	
    the word count in a node:
    
    	getNumberOfWords(self) -> int
    
    # Cite
    
    	@inproceedings{yildiz-etal-2014-constructing,
        	title = "Constructing a {T}urkish-{E}nglish Parallel {T}ree{B}ank",
        	author = {Y{\i}ld{\i}z, Olcay Taner  and
          	Solak, Ercan  and
          	G{\"o}rg{\"u}n, Onur  and
          	Ehsani, Razieh},
        	booktitle = "Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
        	month = jun,
        	year = "2014",
        	address = "Baltimore, Maryland",
        	publisher = "Association for Computational Linguistics",
        	url = "https://www.aclweb.org/anthology/P14-2019",
        	doi = "10.3115/v1/P14-2019",
        	pages = "112--117",
    	}