Data Generator

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For Developers

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

Requirements

• Python 3.7 or higher
• 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.

Git

Install the latest version of Git.

Pip Install

pip3 install NlpToolkit-DataGenerator

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

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

Open project with Pycharm IDE

Steps for opening the cloned project:

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

Detailed Description

• AnnotatedDataSetGenerator
• InstanceGenerator

AnnotatedDataSetGenerator

DataSet yaratmak için AnnotatedDataSetGenerator sınıfı önce üretilir.

AnnotatedDataSetGenerator(self, folder: str, pattern: str, instanceGenerator: InstanceGenerator)

Ardından generate metodu ile DataSet yaratılır.

generate(self) -> DataSet

InstanceGenerator

DataGeneratorlerin InstanceGeneratorlere ihtiyacı vardır. Bunlar bir tek kelimeden bir Instance yaratan sınıflardır.

generateInstanceFromSentence(self, sentence: Sentence, wordIndex: int) -> Instance

NER problemi için NerInstanceGenerator, FeaturedNerInstanceGenerator ve VectorizedNerInstanceGeneratorsınıfı

ShallowParse problemi için ShallowParseInstanceGenerator, FeaturedShallowParseInstanceGenerator ve VectorizedShallowParseInstanceGenerator sınıfı

WSD problemi için SemanticInstanceGenerator, FeaturedSemanticInstanceGenerator ve VectorizedSemanticInstanceGenerator sınıfı

Morphological Disambiguation problemi için FeaturedDisambiguationInstanceGenerator sınıfı

Example Generated DataSet

Word Sense Disambiguation Task

The following Table shows the sample text represented with sense labels and three possible features, namely the root form of the word, the part of speech (POS) tag of the word, and a boolean feature for checking the capital case.

Word	Root	Pos	Capital	...	Tag
Yüzündeki	yüz	Noun	True	...	yüz3
ketçap	ketçap	Noun	False	...	ketçap1
lekesi	leke	Noun	False	...	leke2
yüzdükten	yüz	Verb	False	...	yüz2
sonra	sonra	PCAbl	False	...	sonra1
çıkmış	çık	Verb	False	...	çık10
.	.	Punctuation	False	...	.1

Named Entity Recognition Task

The following Table shows the sample text represented with tag labels and three possible features, namely the root form of the word, the part of speech (POS) tag of the word, and a boolean feature for checking the capital case.

Word	Root	Pos	Capital	...	Tag
Türk	Türk	Noun	True	...	ORGANIZATION
Hava	Hava	Noun	True	...	ORGANIZATION
Yolları	Yol	Noun	True	...	ORGANIZATION
bu	bu	Pronoun	False	...	NONE
Pazartesi'den	Pazartesi	Noun	True	...	TIME
itibaren	itibaren	Adverb	False	...	NONE
İstanbul	İstanbul	Noun	True	...	LOCATION
Ankara	Ankara	Noun	True	...	LOCATION
güzergahı	güzergah	Noun	False	...	NONE
için	için	Adverb	False	...	NONE
indirimli	indirimli	Adjective	False	...	NONE
satışlarını	sat	Noun	False	...	NONE
90	90	Number	False	...	MONEY
TL'den	TL	Noun	True	...	MONEY
başlatacağını	başlat	Noun	False	...	NONE
açıkladı	açıkla	Verb	False	...	NONE
.	.	Punctuation	False	...	NONE

Shallow Parse Task

The following Table shows the sample text represented with chunk labels and three possible features, namely the root form of the word, the part of speech (POS) tag of the word, and a boolean feature for checking the capital case.

Word	Root	Pos	Capital	...	Tag
Türk	Türk	Noun	True	...	ÖZNE
Hava	Hava	Noun	True	...	ÖZNE
Yolları	yol	Noun	True	...	ÖZNE
Salı	Salı	Noun	True	...	ZARF TÜMLECİ
günü	gün	Noun	False	...	ZARF TÜMLECİ
yeni	yeni	Adjective	False	...	NESNE
indirimli	indirimli	Adjective	False	...	NESNE
fiyatlarını	fiyat	Noun	False	...	NESNE
açıkladı	açıkla	Verb	False	...	YÜKLEM
.	.	Punctuation	False	...	HİÇBİRİ

Cite

If you use this resource on your research, please cite the following paper:

@article{acikgoz,
  title={All-words word sense disambiguation for {T}urkish},
  author={O. Açıkg{\"o}z and A. T. G{\"u}rkan and B. Ertopçu and O. Topsakal and B. {\"O}zenç and A. B. Kanburoğlu and {\.{I}}. Çam and B. Avar and G. Ercan and O. T. Y{\i}ld{\i}z},
  journal={2017 International Conference on Computer Science and Engineering (UBMK)},
  year={2017},
  pages={490-495}
}
@inproceedings{ertopcu17,  
	author={B. {Ertopçu} and A. B. {Kanburoğlu} and O. {Topsakal} and O. {Açıkgöz} and A. T. {Gürkan} and B. {Özenç} and İ. {Çam} and B. {Avar} and G. {Ercan} and O. T. {Yıldız}},  
	booktitle={2017 International Conference on Computer Science and Engineering (UBMK)},  title={A new approach for named entity recognition},   
	year={2017},  
	pages={474-479}
}

For Contibutors

Setup.py file

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',

Python files

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()