Word Sense Disambiguation

Task Definition

The task of choosing the correct sense for a word is called word sense disambiguation (WSD). WSD algorithms take an input word w in its context with a fixed set of potential word senses S_w of that input word and produce an output chosen from S_w. In the isolated WSD task, one usually uses the set of senses from a dictionary or theasurus like WordNet.

In the literature, there are actually two variants of the generic WSD task. In the lexical sample task, a small selected set of target words is chosen, along with a set of senses for each target word. For each target word w, a number of corpus sentences (context sentences) are manually labeled with the correct sense of w. In all-words task, systems are given entire sentences and a lexicon with the set of senses for each word in each sentence. Annotators are then asked to disambiguate every word in the text.

In all-words WSD, a classifier is trained to label the words in the text with their set of potential word senses. After giving the sense labels to the words in our training data, the next step is to select a group of features to discriminate different senses for each input word.

The following Table shows an example for the word 'yüz', which can refer to the number '100', to the verb 'swim' or to the noun 'face'.

Sense	Definition
yüz1 (hundred)	The number coming after ninety nine
yüz2 (swim)	move or float in water
yüz3 (face)	face, visage, countenance

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-semantics.jar file into another folder such as Program.
5. Put Turkish-Phrase and Program folders into a parent folder.

Annotation

1. Open sentence-semantics.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 correct sense for that word.
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 Word Sense Disambiguation 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 Word Sense Disambiguation 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-WordSenseDisambiguation-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/WordSenseDisambiguation-Cy.git

Open project with Pycharm IDE

Steps for opening the cloned project:

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

Detailed Description

ParseTree

In order to sense annotate a parse tree, one can use autoSemantic method of the TurkishTreeAutoSemantic class.

parseTree = ...
wordNet = WordNet()
fsm = FsmMorphologicalAnalyzer()
turkishAutoSemantic = TurkishTreeAutoSemantic(wordnet, fsm)
turkishAutoSemantic.autoSemantic()

Sentence

In order to sense annotate a parse tree, one can use autoSemantic method of the TurkishSentenceAutoSemantic class.

sentence = ...
wordNet = WordNet()
fsm = FsmMorphologicalAnalyzer()
turkishAutoSemantic = TurkishSentenceAutoSemantic(wordnet, fsm)
turkishAutoSemantic.autoSemantic()

Cite

@INPROCEEDINGS{8093442,
author={O. {Açıkgöz} and A. T. {Gürkan} and B. {Ertopçu} and O. {Topsakal} and B. {Özenç} and A. B. {Kanburoğlu} and İ. {Çam} and B. {Avar} and G. {Ercan} 		and O. T. {Yıldız}},
booktitle={2017 International Conference on Computer Science and Engineering (UBMK)}, 
title={All-words word sense disambiguation for Turkish}, 
year={2017},
volume={},
number={},
pages={490-495},
doi={10.1109/UBMK.2017.8093442}}

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')