Named Entity Recognition

Task Definition

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

Data Annotation

Preparation

1. Collect a set of sentences to annotate.
2. Each sentence in the collection must be named as xxxx.yyyyy in increasing order. For example, the first sentence to be annotated will be 0001.train, the second 0002.train, etc.
3. Put the sentences in the same folder such as Turkish-Phrase.
4. Build the Java project and put the generated sentence-ner.jar file into another folder such as Program.
5. Put Turkish-Phrase and Program folders into a parent folder.

Annotation

1. Open sentence-ner.jar file.
2. Wait until the data load message is displayed.
3. Click Open button in the Project menu.
4. Choose a file for annotation from the folder Turkish-Phrase.
5. For each word in the sentence, click the word, and choose approprite entity tag from PERSON, ORGANIZATION, LOCATION, TIME, or MONEY tags.
6. Click one of the next buttons to go to other files.

Classification DataSet Generation

After annotating sentences, you can use DataGenerator package to generate classification dataset for the Named Entity Recognition task.

Generation of ML Models

After generating the classification dataset as above, one can use the Classification package to generate machine learning models for the Named Entity Recognition task.

Video Lectures

For Developers

You can also see either Python, Java, C++, Swift, Js, 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-NER-Cy

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

git clone https://github.com/starlangsoftware/NER-Cy.git

Open project with Pycharm IDE

Steps for opening the cloned project:

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

Detailed Description

ParseTree

In order to find the named entities in a parse tree, one uses autoNER method of the TreeAutoNER class.

parseTree = ...
turkishNer = TurkishTreeAutoNER(ViewLayerType.Turkish)
turkishNer.autoNER(parseTree)

Sentence

In order to find the named entities in a simple sentence, one uses autoNER method of the SentenceAutoNER class.

sentence = ...
turkishNer = TurkishSentenceAutoNER()
turkishNer.autoNER(sentence)

Cite

@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}}

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',

3. Package data should be defined and must ibclude pyx, pxd, c and py files.

    package_data={'NGram': ['*.pxd', '*.pyx', '*.c', '*.py']},

4. Setup should include ext_modules with compiler directives.

    ext_modules=cythonize(["NGram/*.pyx"],
                          compiler_directives={'language_level': "3"}),

Cython files

1. 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)

2. 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 = *)

3. 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):

4. 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.
        """

5. Function names should follow caml case.

    cpdef addItem(self, str item):

6. Local variables should follow snake case.

	det = 1.0
	copy_of_matrix = copy.deepcopy(self)

7. Variable types should be defined for function parameters, class variables.

    cpdef double getValue(self, int rowNo, int colNo):

8. Local variables should be defined with types.

    cpdef sortDefinitions(self):
        cdef int i, j
        cdef str tmp

9. For abstract methods, use ABC package and declare them with @abstractmethod.

    @abstractmethod
    def train(self, train_set: list[Tensor]):
        pass

10. For private methods, use __ as prefix in their names.

    cpdef list __linearRegressionOnCountsOfCounts(self, list countsOfCounts)

11. 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

12. Write __repr__ class methods as toString methods
13. Write getter and setter class methods.

    cpdef int getN(self)
    cpdef setN(self, int N)

14. 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)

15. Extend test classes from unittest and use separate unit test methods.

class NGramTest(unittest.TestCase):

    def test_GetCountSimple(self):

16. For undefined types use object as type in the type declarations.

cdef class WordNet:

    cdef object __syn_set_list
    cdef object __literal_list

17. For boolean types use bint as type in the type declarations.

	cdef bint is_done

18. Enumerated types should be used when necessary as enum classes, and should be declared in py files.

class AttributeType(Enum):
    """
    Continuous Attribute
    """
    CONTINUOUS = auto()
    """

19. Resource files should be taken from pkg_recources package.

	fileName = pkg_resources.resource_filename(__name__, 'data/turkish_wordnet.xml')