When one talks about the “success” of a Natural Language Processing solution, they often refer to its ability to analyse the semantic and syntactic structure of a given sentence. Such a solution is expected to be able to understand both the linear and hierarchical order of the words in a sentence, unveil embedded structures, illustrate syntactical relationships and have a firm grasp of the argument structure. In order to meet the expectations, cutting edge Natural Language Processing systems like parsers, POS taggers or machine translation systems make use of syntactically or semantically annotated treebanks. Such treebanks offer a deep look through the surface and into the logical form of sentences.

Annotated treebanks can be categorised as constituency treebanks and dependency treebanks. Constituency treebanks offers clarity through resolving structural ambiguities, and successfully illustrates the syntagmatic relations like adjunct, complement, predicate, internal argument, external argument and such.

The very first comprehensive annotated treebank, the Penn Treebank, was created for the English language and offers 40,000 annotated sentences. Following the Penn Treebank, numerous treebanks annotated for constituency structures were developed in different languages including French, German, Finnish, Hungarian, Chinese and Arabic.

You can also see Cython, Java, C, C++, Swift, Js, or C# repository.

• Python 3.7 or higher
• Git

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.

Install the latest version of Git.

pip3 install NlpToolkit-ParseTree

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 ParseTree will be created. Or you can use below link for exploring the code:

git clone https://github.com/starlangsoftware/ParseTree-Py.git

Steps for opening the cloned project:

• Start IDE
• Select File | Open from main menu
• Choose ParseTree-Py file
• Select open as project option
• Couple of seconds, dependencies will be downloaded.

• TreeBank
• ParseTree

To load a TreeBank composed of saved ParseTrees from a folder:

TreeBank(self, folder: str = None)

To load trees with a specified pattern from a folder of trees:

TreeBank(self, folder: str = None, pattern: str = None)

the line above is used. For example,

a = TreeBank("/mypath");

the line below is used to load trees under the folder "mypath" which is under the current folder. If only the trees with ".train" extension under the same folder are to be loaded:

a = TreeBank("/mypath", ".train");

the line below is used.

To iterate over the trees after the TreeBank is loaded:

for i in range(a.size()):
	p = a.get(i);

a block of code like this can be useful.

To load a saved ParseTree:

ParseTree(fileName: str)

is used. Usually it is more useful to load a TreeBank as explained above than loading the ParseTree one by one.

To find the node number of a ParseTree:

nodeCount() -> int

leaf number of a ParseTree:

leafCount() -> int

number of words in a ParseTree:

wordCount(excludeStopWords: bool) -> int

above methods can be used.

@INPROCEEDINGS{9259873,
author={N. {Kara} and B. {Marşan} and M. {Özçelik} and B. N. {Arıcan} and A. {Kuzgun} and N. {Cesur} and D. B. {Aslan} and O. T. {Yıldız}},
booktitle={2020 Innovations in Intelligent Systems and Applications Conference (ASYU)}, 
title={Creating A Syntactically Felicitous Constituency Treebank For Turkish}, 
year={2020},
volume={},
number={},
pages={1-6},
doi={10.1109/ASYU50717.2020.9259873}}

1. Do not forget to set package list. All subfolders should be added to the package list.

    packages=['Classification', 'Classification.Model', 'Classification.Model.DecisionTree',
              'Classification.Model.Ensemble', 'Classification.Model.NeuralNetwork',
              'Classification.Model.NonParametric', 'Classification.Model.Parametric',
              'Classification.Filter', 'Classification.DataSet', 'Classification.Instance', 'Classification.Attribute',
              'Classification.Parameter', 'Classification.Experiment',
              'Classification.Performance', 'Classification.InstanceList', 'Classification.DistanceMetric',
              'Classification.StatisticalTest', 'Classification.FeatureSelection'],

2. Package name should be lowercase and only may include _ character.

    name='nlptoolkit_math',

1. Do not forget to comment each function.

    def __broadcast_shape(self, shape1: Tuple[int, ...], shape2: Tuple[int, ...]) -> Tuple[int, ...]:
        """
        Determines the broadcasted shape of two tensors.

        :param shape1: Tuple representing the first tensor shape.
        :param shape2: Tuple representing the second tensor shape.
        :return: Tuple representing the broadcasted shape.
        """

2. Function names should follow caml case.

    def addItem(self, item: str):

3. Local variables should follow snake case.

	det = 1.0
	copy_of_matrix = copy.deepcopy(self)

4. Class variables should be declared in each file.

class Eigenvector(Vector):
    eigenvalue: float

5. Variable types should be defined for function parameters and class variables.

    def getIndex(self, item: str) -> int:

6. For abstract methods, use ABC package and declare them with @abstractmethod.

    @abstractmethod
    def train(self, train_set: list[Tensor]):
        pass

7. For private methods, use __ as prefix in their names.

    def __infer_shape(self, data: Union[List, List[List], List[List[List]]]) -> Tuple[int, ...]:

8. For private class variables, use __ as prefix in their names.

class Matrix(object):
    __row: int
    __col: int
    __values: list[list[float]]

9. Write __repr__ class methods as toString methods
10. Write getter and setter class methods.

    def getOptimizer(self) -> Optimizer:
        return self.optimizer
    def setValue(self, value: Optional[Tensor]) -> None:
        self._value = value

11. If there are multiple constructors for a class, define them as constructor1, constructor2, ..., then from the original constructor call these methods.

    def constructor1(self):
        self.__values = []
        self.__size = 0

    def constructor2(self, values: list):
        self.__values = values.copy()
        self.__size = len(values)

    def __init__(self,
                 valuesOrSize=None,
                 initial=None):
        if valuesOrSize is None:
            self.constructor1()
        elif isinstance(valuesOrSize, list):
            self.constructor2(valuesOrSize)

12. Extend test classes from unittest and use separate unit test methods.

class TensorTest(unittest.TestCase):

    def test_inferred_shape(self):
        a = Tensor([[1.0, 2.0], [3.0, 4.0]])
        self.assertEqual((2, 2), a.getShape())

    def test_shape(self):
        a = Tensor([1.0, 2.0, 3.0])
        self.assertEqual((3, ), a.getShape())

13. Enumerated types should be used when necessary as enum classes.

class AttributeType(Enum):
    """
    Continuous Attribute
    """
    CONTINUOUS = auto()
    """
    Discrete Attribute
    """
    DISCRETE = auto()