In named entity recognition, one tries to find the strings within a text that correspond to proper names (excluding TIME and MONEY) and classify the type of entity denoted by these strings. The problem is difficult partly due to the ambiguity in sentence segmentation; one needs to extract which words belong to a named entity, and which not. Another difficulty occurs when some word may be used as a name of either a person, an organization or a location. For example, Deniz may be used as the name of a person, or - within a compound - it can refer to a location Marmara Denizi 'Marmara Sea', or an organization Deniz Taşımacılık 'Deniz Transportation'.
The standard approach for NER is a word-by-word classification, where the classifier is trained to label the words in the text with tags that indicate the presence of particular kinds of named entities. After giving the class labels (named entity tags) to our training data, the next step is to select a group of features to discriminate different named entities for each input word.
[ORG Türk Hava Yolları] bu [TIME Pazartesi'den] itibaren [LOC İstanbul] [LOC Ankara] hattı için indirimli satışlarını [MONEY 90 TL'den] başlatacağını açıkladı.
[ORG Turkish Airlines] announced that from this [TIME Monday] on it will start its discounted fares of [MONEY 90TL] for [LOC İstanbul] [LOC Ankara] route.
See the Table below for typical generic named entity types.
| Tag | Sample Categories |
|---|---|
| PERSON | people, characters |
| ORGANIZATION | companies, teams |
| LOCATION | regions, mountains, seas |
| TIME | time expressions |
| MONEY | monetarial expressions |
You can also see Cython, Java, C, C++, Swift, Js, or C# repository.
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-NamedEntityRecognition
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 github.com/starlangsoftware/TurkishNamedEntityRecognition-Py.git
Steps for opening the cloned project:
- Start IDE
- Select File | Open from main menu
- Choose
TurkishNamedEntityRecognition-Py/file - Select open as project option
Bir Gazetter yüklemek için
Gazetteer(self, name: str, fileName: str)
Hazır Gazetteerleri kullanmak için
AutoNER()
Bir Gazetteer'de bir kelime var mı diye kontrol etmek için
contains(self, word: str) -> bool
@INPROCEEDINGS{8093439,
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},
volume={},
number={},
pages={474-479},
doi={10.1109/UBMK.2017.8093439}}
- 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'],
- Package name should be lowercase and only may include _ character.
name='nlptoolkit_math',
- 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.
"""
- Function names should follow caml case.
def addItem(self, item: str):
- Local variables should follow snake case.
det = 1.0
copy_of_matrix = copy.deepcopy(self)
- Class variables should be declared in each file.
class Eigenvector(Vector):
eigenvalue: float
- Variable types should be defined for function parameters and class variables.
def getIndex(self, item: str) -> int:
- For abstract methods, use ABC package and declare them with @abstractmethod.
@abstractmethod
def train(self, train_set: list[Tensor]):
pass
- For private methods, use __ as prefix in their names.
def __infer_shape(self, data: Union[List, List[List], List[List[List]]]) -> Tuple[int, ...]:
- For private class variables, use __ as prefix in their names.
class Matrix(object):
__row: int
__col: int
__values: list[list[float]]
- Write __repr__ class methods as toString methods
- Write getter and setter class methods.
def getOptimizer(self) -> Optimizer:
return self.optimizer
def setValue(self, value: Optional[Tensor]) -> None:
self._value = value
- 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)
- 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())
- Enumerated types should be used when necessary as enum classes.
class AttributeType(Enum):
"""
Continuous Attribute
"""
CONTINUOUS = auto()
"""
Discrete Attribute
"""
DISCRETE = auto()