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 Python, 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-ParseTree-Cy
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-Cy.git
Steps for opening the cloned project:
- Start IDE
- Select File | Open from main menu
- Choose
ParseTree-Cyfile - Select open as project option
- Couple of seconds, dependencies will be downloaded.
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}}
- 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',
- Package data should be defined and must ibclude pyx, pxd, c and py files.
package_data={'NGram': ['*.pxd', '*.pyx', '*.c', '*.py']},
- Setup should include ext_modules with compiler directives.
ext_modules=cythonize(["NGram/*.pyx"],
compiler_directives={'language_level': "3"}),
- Define the class variables and class methods in the pxd file.
cdef class DiscreteDistribution(dict):
cdef float __sum
cpdef addItem(self, str item)
cpdef removeItem(self, str item)
cpdef addDistribution(self, DiscreteDistribution distribution)
- For default values in class method declarations, use *.
cpdef list constructIdiomLiterals(self, FsmMorphologicalAnalyzer fsm, MorphologicalParse morphologicalParse1,
MetamorphicParse metaParse1, MorphologicalParse morphologicalParse2,
MetamorphicParse metaParse2, MorphologicalParse morphologicalParse3 = *,
MetamorphicParse metaParse3 = *)
- Define the class name as cdef, class methods as cpdef, and __init__ as def.
cdef class DiscreteDistribution(dict):
def __init__(self, **kwargs):
"""
A constructor of DiscreteDistribution class which calls its super class.
"""
super().__init__(**kwargs)
self.__sum = 0.0
cpdef addItem(self, str item):
- Do not forget to comment each function.
cpdef addItem(self, str item):
"""
The addItem method takes a String item as an input and if this map contains a mapping for the item it puts the
item with given value + 1, else it puts item with value of 1.
PARAMETERS
----------
item : string
String input.
"""
- Function names should follow caml case.
cpdef addItem(self, str item):
- Local variables should follow snake case.
det = 1.0
copy_of_matrix = copy.deepcopy(self)
- Variable types should be defined for function parameters, class variables.
cpdef double getValue(self, int rowNo, int colNo):
- Local variables should be defined with types.
cpdef sortDefinitions(self):
cdef int i, j
cdef str tmp
- 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.
cpdef list __linearRegressionOnCountsOfCounts(self, list countsOfCounts)
- For private class variables, use __ as prefix in their names.
cdef class NGram:
cdef int __N
cdef double __lambda1, __lambda2
cdef bint __interpolated
cdef set __vocabulary
cdef list __probability_of_unseen
- Write __repr__ class methods as toString methods
- Write getter and setter class methods.
cpdef int getN(self)
cpdef setN(self, int N)
- If there are multiple constructors for a class, define them as constructor1, constructor2, ..., then from the original constructor call these methods.
cdef class NGram:
cpdef constructor1(self, int N, list corpus):
cpdef constructor2(self, str fileName):
def __init__(self,
NorFileName,
corpus=None):
if isinstance(NorFileName, int):
self.constructor1(NorFileName, corpus)
else:
self.constructor2(NorFileName)
- Extend test classes from unittest and use separate unit test methods.
class NGramTest(unittest.TestCase):
def test_GetCountSimple(self):
- For undefined types use object as type in the type declarations.
cdef class WordNet:
cdef object __syn_set_list
cdef object __literal_list
- For boolean types use bint as type in the type declarations.
cdef bint is_done
- Enumerated types should be used when necessary as enum classes, and should be declared in py files.
class AttributeType(Enum):
"""
Continuous Attribute
"""
CONTINUOUS = auto()
"""
- Resource files should be taken from pkg_recources package.
fileName = pkg_resources.resource_filename(__name__, 'data/turkish_wordnet.xml')