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 Python, Java, C++, C, Js, Php, Swift, 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-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 DataStructure will be created. Or you can use below link for exploring the code:
git clone https://github.com/starlangsoftware/AnnotatedTree-Cy.git
Steps for opening the cloned project:
- Start IDE
- Select File | Open from main menu
- Choose
AnnotatedTree-Cyfile - 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',
- 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')