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.
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.
You can also see Cython, Java, C++, C, Js, Swift, Php, 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-AnnotatedTree
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-Py.git
Steps for opening the cloned project:
- Start IDE
- Select File | Open from main menu
- Choose
AnnotatedTree-Pyfile - Select open as project option
- Couple of seconds, dependencies will be downloaded.
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);
....
}
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.
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
@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",
}
- 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()