Named Entity Recognition System for Hindi Language: A Hybrid ApproachWaqas Tariq
Named Entity Recognition (NER) is a major early step in Natural Language Processing (NLP) tasks like machine translation, text to speech synthesis, natural language understanding etc. It seeks to classify words which represent names in text into predefined categories like location, person-name, organization, date, time etc. In this paper we have used a combination of machine learning and Rule based approaches to classify named entities. The paper introduces a hybrid approach for NER. We have experimented with Statistical approaches like Conditional Random Fields (CRF) & Maximum Entropy (MaxEnt) and Rule based approach based on the set of linguistic rules. Linguistic approach plays a vital role in overcoming the limitations of statistical models for morphologically rich language like Hindi. Also the system uses voting method to improve the performance of the NER system. Keywords: NER, MaxEnt, CRF, Rule base, Voting, Hybrid approach
Improvement in Quality of Speech associated with Braille codes - A Reviewinscit2006
J. Anurag, P. Nupur and Agrawal, S.S.
School of Information Technology, Guru Gobind Singh Indraprastha University, Delhi, India
Centre for Development of Advanced Computing, Noida, India
Intent Classifier with Facebook fastText
Facebook Developer Circle, Malang
22 February 2017
This is slide for Facebook Developer Circle meetup.
This is for beginner.
Named Entity Recognition System for Hindi Language: A Hybrid ApproachWaqas Tariq
Named Entity Recognition (NER) is a major early step in Natural Language Processing (NLP) tasks like machine translation, text to speech synthesis, natural language understanding etc. It seeks to classify words which represent names in text into predefined categories like location, person-name, organization, date, time etc. In this paper we have used a combination of machine learning and Rule based approaches to classify named entities. The paper introduces a hybrid approach for NER. We have experimented with Statistical approaches like Conditional Random Fields (CRF) & Maximum Entropy (MaxEnt) and Rule based approach based on the set of linguistic rules. Linguistic approach plays a vital role in overcoming the limitations of statistical models for morphologically rich language like Hindi. Also the system uses voting method to improve the performance of the NER system. Keywords: NER, MaxEnt, CRF, Rule base, Voting, Hybrid approach
Improvement in Quality of Speech associated with Braille codes - A Reviewinscit2006
J. Anurag, P. Nupur and Agrawal, S.S.
School of Information Technology, Guru Gobind Singh Indraprastha University, Delhi, India
Centre for Development of Advanced Computing, Noida, India
Intent Classifier with Facebook fastText
Facebook Developer Circle, Malang
22 February 2017
This is slide for Facebook Developer Circle meetup.
This is for beginner.
Introduction to Ontology Engineering with Fluent Editor 2014Cognitum
An introductory course for Ontology Engineering using Controlled Natural Language. Fluent Editor (FE) is an ontology editor that is a tool for editing and manipulating ontologies. The main feature of Fluent Editor is that it uses controlled natural language (CNL) to communicate with a user. Communication with CNL is a more suitable for human users alternative to XML-based OWL editors.
A deep analysis of Multi-word Expression and Machine TranslationLifeng (Aaron) Han
A deep analysis of Multi-word Expression and Machine Translation. Faculty research open day. DCU, Dublin. 2019.
Including MWE identification, MT with radical, MTE.
Named Entity Recognition for Telugu Using Conditional Random FieldWaqas Tariq
Named Entity (NE) recognition is a task in which proper nouns and numerical information are extracted from documents and are classified into predefined categories such as Person names, Organization names , Location names, miscellaneous(Date and others). It is a key technology of Information Extraction, Question Answering system, Machine Translations, Information Retrial etc. This paper reports about the development of a NER system for Telugu using Conditional Random field (CRF). Though this state of the art machine learning technique has been widely applied to NER in several well-studied languages, the use of this technique to Telugu languages is very new. The system makes use of the different contextual information of the words along with the variety of features that are helpful in predicting the four different named entities (NE) classes, such as Person name, Location name, Organization name, miscellaneous (Date and others). Keywords: Named entity, Conditional Random field, NE, CRF, NER, named entity recognition
Improving Document Clustering by Eliminating Unnatural LanguageJinho Choi
Technical documents contain a fair amount of unnatural language, such as tables, formulas, and pseudo-code. Unnatural language can be an important factor of confusing existing NLP tools. This paper presents an effective method of distinguishing unnatural language from natural language, and evaluates the impact of unnatural language detection on NLP tasks such as document clustering. We view this problem as an information extraction task and build a multiclass classification model identifying unnatural language components into four categories. First, we create a new annotated corpus by collecting slides and papers in various formats, PPT, PDF, and HTML, where unnatural language components are annotated into four categories. We then explore features available from plain text to build a statistical model that can handle any format as long as it is converted into plain text. Our experiments show that removing unnatural language components gives an absolute improvement in document clustering by up to 15%. Our corpus and tool are publicly available.
The objective of the research is to classify the serial-verb constructions in Thai automatically by using the
word classes from Thai WordNet to classify verbs in the sentence. Due to the Thai language has the extendto-
the-right structure and put the adjective after the noun. Its overall grammar characteristic is the
"Subject-Verb-Object" or SVO type. And Thai language can be communicated using one verb after another
within the same sentence, that we called "Serial Verb". Today we already have many researches about this
serial-verb constructions, but no research is about its automatic classification.
Dr Alessandro Seganti from Cognitum presented basics of Semantic Technologies, OntorionCNL, Ontorion Semantic Framework and Fluent Editor during International Conference on Computer Science -- Research and Applications IBIZA 2014, UMCS Lublin.
To learn more visit: http://www.cognitum.eu/semantics/
A COMPREHENSIVE ANALYSIS OF STEMMERS AVAILABLE FOR INDIC LANGUAGES ijnlc
Stemming is the process of term conflation. It conflates all the word variants to a common form called as stem. It plays significant role in numerous Natural Language Processing (NLP) applications like morphological analysis, parsing, document summarization, text classification, part-of-speech tagging, question-answering system, machine translation, word sense disambiguation, information retrieval (IR), etc. Each of these tasks requires some pre-processing to be done. Stemming is one of the important building blocks for all these applications. This paper, presents an overview of various stemming techniques, evaluation criteria for stemmers and various existing stemmers for Indic languages.
Named Entity Recognition using Hidden Markov Model (HMM)kevig
Named Entity Recognition (NER) is the subtask of Natural Language Processing (NLP) which is the branch of artificial intelligence. It has many applications mainly in machine translation, text to speech synthesis, natural language understanding, Information Extraction, Information retrieval, question answering etc. The aim of NER is to classify words into some predefined categories like location name, person name, organization name, date, time etc. In this paper we describe the Hidden Markov Model (HMM) based approach of machine learning in detail to identify the named entities. The main idea behind the use of HMM model for building NER system is that it is language independent and we can apply this system for any language domain. In our NER system the states are not fixed means it is of dynamic in nature one can use it according to their interest. The corpus used by our NER system is also not domain specific
Presentation of Domain Specific Question Answering System Using N-gram Approach.Tasnim Ara Islam
Design an application for a domain specific question answering system. Built a solution for finding answers of factoid questions by using N-gram Mining Approach. Calculated percentage about the related answers for the specific question. Built this application in Java platform.
Unsupervised Software-Specific Morphological Forms Inference from Informal Di...Chunyang Chen
The paper accepted on ICSE'17 and TSE'19. https://se-thesaurus.appspot.com/ https://pypi.org/project/DomainThesaurus/ Informal discussions on social platforms (e.g., Stack Overflow) accumulates a large body of programming knowledge in natural language text. Natural language process (NLP) techniques can be exploited to harvest this knowledge base for software engineering tasks. To make an effective use of NLP techniques, consistent vocabulary is essential. Unfortunately, the same concepts are often intentionally or accidentally mentioned in many different morphological forms in informal discussions, such as abbreviations, synonyms and misspellings. Existing techniques to deal with such morphological forms are either designed for general English or predominantly rely on domain-specific lexical rules. A thesaurus of software-specific terms and commonlyused morphological forms is desirable for normalizing software engineering text, but very difficult to build manually. In this work, we propose an automatic approach to build such a thesaurus. Our approach identifies software-specific terms by contrasting software-specific and general corpuses, and infers morphological forms of software-specific terms by combining distributed word semantics, domain-specific lexical rules and transformations, and graph analysis of morphological relations. We evaluate the coverage and accuracy of the resulting thesaurus against community-curated lists of software-specific terms, abbreviations and synonyms. We also manually examine the correctness of the identified abbreviations and synonyms in our thesaurus. We demonstrate the usefulness of our thesaurus in a case study of normalizing questions from Stack Overflow and CodeProject.
A New Concept Extraction Method for Ontology Construction From Arabic TextCSCJournals
Ontology is one of the most popular representation model used for knowledge representation, sharing and reusing. The Arabic language has complex morphological, grammatical, and semantic aspects. Due to complexity of Arabic language, automatic Arabic terminology extraction is difficult. In addition, concept extraction from Arabic documents has been challenging research area, because, as opposed to term extraction, concept extraction are more domain related and more selective. In this paper, we present a new concept extraction method for Arabic ontology construction, which is the part of our ontology construction framework. A new method to extract domain relevant single and multi-word concepts in the domain has been proposed, implemented and evaluated. Our method combines linguistic, statistical information and domain knowledge. It first uses linguistic patterns based on POS tags to extract concept candidates, and then stop words filter is implemented to filter unwanted strings. To determine relevance of these candidates within the domain, different statistical measures and new domain relevance measure are implemented for first time for Arabic language. To enhance the performance of concept extraction, a domain knowledge will be integrated into the module. The concepts scores are calculated according to their statistical values and domain knowledge values. In order to evaluate the performance of the method, precision scores were calculated. The results show the high effectiveness of the proposed approach to extract concepts for Arabic ontology construction.
Introduction to Ontology Engineering with Fluent Editor 2014Cognitum
An introductory course for Ontology Engineering using Controlled Natural Language. Fluent Editor (FE) is an ontology editor that is a tool for editing and manipulating ontologies. The main feature of Fluent Editor is that it uses controlled natural language (CNL) to communicate with a user. Communication with CNL is a more suitable for human users alternative to XML-based OWL editors.
A deep analysis of Multi-word Expression and Machine TranslationLifeng (Aaron) Han
A deep analysis of Multi-word Expression and Machine Translation. Faculty research open day. DCU, Dublin. 2019.
Including MWE identification, MT with radical, MTE.
Named Entity Recognition for Telugu Using Conditional Random FieldWaqas Tariq
Named Entity (NE) recognition is a task in which proper nouns and numerical information are extracted from documents and are classified into predefined categories such as Person names, Organization names , Location names, miscellaneous(Date and others). It is a key technology of Information Extraction, Question Answering system, Machine Translations, Information Retrial etc. This paper reports about the development of a NER system for Telugu using Conditional Random field (CRF). Though this state of the art machine learning technique has been widely applied to NER in several well-studied languages, the use of this technique to Telugu languages is very new. The system makes use of the different contextual information of the words along with the variety of features that are helpful in predicting the four different named entities (NE) classes, such as Person name, Location name, Organization name, miscellaneous (Date and others). Keywords: Named entity, Conditional Random field, NE, CRF, NER, named entity recognition
Improving Document Clustering by Eliminating Unnatural LanguageJinho Choi
Technical documents contain a fair amount of unnatural language, such as tables, formulas, and pseudo-code. Unnatural language can be an important factor of confusing existing NLP tools. This paper presents an effective method of distinguishing unnatural language from natural language, and evaluates the impact of unnatural language detection on NLP tasks such as document clustering. We view this problem as an information extraction task and build a multiclass classification model identifying unnatural language components into four categories. First, we create a new annotated corpus by collecting slides and papers in various formats, PPT, PDF, and HTML, where unnatural language components are annotated into four categories. We then explore features available from plain text to build a statistical model that can handle any format as long as it is converted into plain text. Our experiments show that removing unnatural language components gives an absolute improvement in document clustering by up to 15%. Our corpus and tool are publicly available.
The objective of the research is to classify the serial-verb constructions in Thai automatically by using the
word classes from Thai WordNet to classify verbs in the sentence. Due to the Thai language has the extendto-
the-right structure and put the adjective after the noun. Its overall grammar characteristic is the
"Subject-Verb-Object" or SVO type. And Thai language can be communicated using one verb after another
within the same sentence, that we called "Serial Verb". Today we already have many researches about this
serial-verb constructions, but no research is about its automatic classification.
Dr Alessandro Seganti from Cognitum presented basics of Semantic Technologies, OntorionCNL, Ontorion Semantic Framework and Fluent Editor during International Conference on Computer Science -- Research and Applications IBIZA 2014, UMCS Lublin.
To learn more visit: http://www.cognitum.eu/semantics/
A COMPREHENSIVE ANALYSIS OF STEMMERS AVAILABLE FOR INDIC LANGUAGES ijnlc
Stemming is the process of term conflation. It conflates all the word variants to a common form called as stem. It plays significant role in numerous Natural Language Processing (NLP) applications like morphological analysis, parsing, document summarization, text classification, part-of-speech tagging, question-answering system, machine translation, word sense disambiguation, information retrieval (IR), etc. Each of these tasks requires some pre-processing to be done. Stemming is one of the important building blocks for all these applications. This paper, presents an overview of various stemming techniques, evaluation criteria for stemmers and various existing stemmers for Indic languages.
Named Entity Recognition using Hidden Markov Model (HMM)kevig
Named Entity Recognition (NER) is the subtask of Natural Language Processing (NLP) which is the branch of artificial intelligence. It has many applications mainly in machine translation, text to speech synthesis, natural language understanding, Information Extraction, Information retrieval, question answering etc. The aim of NER is to classify words into some predefined categories like location name, person name, organization name, date, time etc. In this paper we describe the Hidden Markov Model (HMM) based approach of machine learning in detail to identify the named entities. The main idea behind the use of HMM model for building NER system is that it is language independent and we can apply this system for any language domain. In our NER system the states are not fixed means it is of dynamic in nature one can use it according to their interest. The corpus used by our NER system is also not domain specific
Presentation of Domain Specific Question Answering System Using N-gram Approach.Tasnim Ara Islam
Design an application for a domain specific question answering system. Built a solution for finding answers of factoid questions by using N-gram Mining Approach. Calculated percentage about the related answers for the specific question. Built this application in Java platform.
Unsupervised Software-Specific Morphological Forms Inference from Informal Di...Chunyang Chen
The paper accepted on ICSE'17 and TSE'19. https://se-thesaurus.appspot.com/ https://pypi.org/project/DomainThesaurus/ Informal discussions on social platforms (e.g., Stack Overflow) accumulates a large body of programming knowledge in natural language text. Natural language process (NLP) techniques can be exploited to harvest this knowledge base for software engineering tasks. To make an effective use of NLP techniques, consistent vocabulary is essential. Unfortunately, the same concepts are often intentionally or accidentally mentioned in many different morphological forms in informal discussions, such as abbreviations, synonyms and misspellings. Existing techniques to deal with such morphological forms are either designed for general English or predominantly rely on domain-specific lexical rules. A thesaurus of software-specific terms and commonlyused morphological forms is desirable for normalizing software engineering text, but very difficult to build manually. In this work, we propose an automatic approach to build such a thesaurus. Our approach identifies software-specific terms by contrasting software-specific and general corpuses, and infers morphological forms of software-specific terms by combining distributed word semantics, domain-specific lexical rules and transformations, and graph analysis of morphological relations. We evaluate the coverage and accuracy of the resulting thesaurus against community-curated lists of software-specific terms, abbreviations and synonyms. We also manually examine the correctness of the identified abbreviations and synonyms in our thesaurus. We demonstrate the usefulness of our thesaurus in a case study of normalizing questions from Stack Overflow and CodeProject.
A New Concept Extraction Method for Ontology Construction From Arabic TextCSCJournals
Ontology is one of the most popular representation model used for knowledge representation, sharing and reusing. The Arabic language has complex morphological, grammatical, and semantic aspects. Due to complexity of Arabic language, automatic Arabic terminology extraction is difficult. In addition, concept extraction from Arabic documents has been challenging research area, because, as opposed to term extraction, concept extraction are more domain related and more selective. In this paper, we present a new concept extraction method for Arabic ontology construction, which is the part of our ontology construction framework. A new method to extract domain relevant single and multi-word concepts in the domain has been proposed, implemented and evaluated. Our method combines linguistic, statistical information and domain knowledge. It first uses linguistic patterns based on POS tags to extract concept candidates, and then stop words filter is implemented to filter unwanted strings. To determine relevance of these candidates within the domain, different statistical measures and new domain relevance measure are implemented for first time for Arabic language. To enhance the performance of concept extraction, a domain knowledge will be integrated into the module. The concepts scores are calculated according to their statistical values and domain knowledge values. In order to evaluate the performance of the method, precision scores were calculated. The results show the high effectiveness of the proposed approach to extract concepts for Arabic ontology construction.
ON THE UTILITY OF A SYLLABLE-LIKE SEGMENTATION FOR LEARNING A TRANSLITERATION...cscpconf
Source and target word segmentation and alignment is a primary step in the statistical learning of a Transliteration. Here, we analyze the benefit of a syllable-like segmentation approach for learning a transliteration from English to an Indic language, which aligns the training set word pairs in terms of sub-syllable-like units instead of individual character units. While this has been found useful in the case of dealing with Out-of-vocabulary words in English-Chinese in the presence of multiple target dialects, we asked if this would be true for Indic languages which are simpler in their phonetic representation and pronunciation. We expected this syllable-like method to perform marginally better, but we found instead that even though our proposed approach improved the Top-1 accuracy, the individual-character-unit alignment model
somewhat outperformed our approach when the Top-10 results of the system were re-ranked using language modeling approaches. Our experiments were conducted for English to Telugu transliteration (our method will apply equally well to most written Indic languages); our training consisted of a syllable-like segmentation and alignment of a large training set, on which we built a statistical model by modifying a previous character-level maximum entropy based Transliteration learning system due to Kumaran and Kellner; our testing consisted of using the same segmentation of a test English word, followed by applying the model, and reranking the resulting top 10 Telugu words. We also report the dataset creation and selection since standard datasets are not available.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
French machine reading for question answeringAli Kabbadj
This paper proposes to unlock the main barrier to machine reading and comprehension French natural language texts. This open the way to machine to find to a question a precise answer buried in the mass of unstructured French texts. Or to create a universal French chatbot. Deep learning has produced extremely promising results for various tasks in natural language understanding particularly topic classification, sentiment analysis, question answering, and language translation. But to be effective Deep Learning methods need very large training da-tasets. Until now these technics cannot be actually used for French texts Question Answering (Q&A) applications since there was not a large Q&A training dataset. We produced a large (100 000+) French training Dataset for Q&A by translating and adapting the English SQuAD v1.1 Dataset, a GloVe French word and character embed-ding vectors from Wikipedia French Dump. We trained and evaluated of three different Q&A neural network ar-chitectures in French and carried out a French Q&A models with F1 score around 70%.
Dear students get fully solved SMU MBA Fall 2014 assignments
Send your semester & Specialization name to our mail id :
“ help.mbaassignments@gmail.com ”
or
Call us at : 08263069601
Tamil-English Document Translation Using Statistical Machine Translation Appr...baskaran_md
The Paper presents a new method for translating a text document from Tamil to English. Our method is based on the Statistical Machine Translation Approach, combined with the Morphological Analysis, due to the fact that Tamil is a highly-inflected language. This paper presents a slight modification in SMT to make the approach more efficient and effective, and the experimental results have proven the method to be speed and accurate in the translation process.
Machine translation from English to HindiRajat Jain
Machine translation a part of natural language processing.The algorithm suggested is word based algorithm.We have done Translation from English to Hindi
submitted by
Garvita Sharma,10103467,B3
Rajat Jain,10103571,B6
EXTENDING THE KNOWLEDGE OF THE ARABIC SENTIMENT CLASSIFICATION USING A FOREIG...ijnlc
This article introduces a methodology for analyzing sentiment in Arabic text using a global foreign lexical
source. Our method leverages the available resource in another language such as the SentiWordNet in
English to the limited language resource that is Arabic. The knowledge that is taken from the external
resource will be injected into the feature model whilethe machine-learning-based classifier is trained. The
first step of our method is to build the bag-of-words (BOW) model of the Arabic text. The second step
calculates the score of polarity using translation machine technique and English SentiWordNet. The scores
for each text will be added to the model in three pairs for objective, positive, and negative. The last step of
our method involves training the ML classifier on that model to predict the sentiment of the Arabic text.
Our method increases the performance compared with the baseline model that is BOW in most cases. In
addition, it seems a viable approach to sentiment analysis in Arabic text where there is limitation of the
available resource.
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
“ help.mbaassignments@gmail.com ”
or
Call us at : 08263069601
The I in PRIMM - Code Comprehension and QuestioningSue Sentance
Slides from a talk given at the CAS London conference on 29th February 2020. Discusses the teaching of computer programming using PRIMM and in particular, the Investigate stage. Looks at the Block Model and how we can explore students' understanding by asking a range of different questions.
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
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or
Call us at : 08263069601
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Normal Labour/ Stages of Labour/ Mechanism of LabourWasim Ak
Normal labor is also termed spontaneous labor, defined as the natural physiological process through which the fetus, placenta, and membranes are expelled from the uterus through the birth canal at term (37 to 42 weeks
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
1. Final Year Presentation
(CSF 35104)
Title : Arabic Grammar Mobile Application (M-Nahu)
Name : Amirul Safwan Bin Ismail
Matrik No : 044301
Course : ISMSK (PP)
Supervisor : En Mohd Khalid bin Awang
2. Background
Arabic language is one of the language that is used as a communication
language.
Arabic language like other language such as Malay and English, has its own
grammars
The person who learns Arabic grammar usually uses books as the main
reference
M-Nahu is a mobile learning application that will help user in learning Arabic
grammar such as searching the words, do exercise and make a revision
3. Objectives
a) To design the application that suitable for users from the various ages
b) To develop an application that can help user in learning the Arabic grammar
easily using a mobile phone
c) To test the application whether it can help the user in understanding the
Arabic grammar.
4. Scope
a) Mobile users
can register into the application by input their personal information
can search the words that was used in specific grammars
can answer the quiz that was provided in the application based on certain topics
can request the new words to the admin if not available in the application
can bookmarks the content of the application for future references
5. Scope
b) Admin
can add new words into the application
can add new topics into the application
can add new question for quiz in the application
accepting the words request that made by the users
7. Framework Design
Users
Arabic Grammar Mobile
Application Database
Login
Bookmarks The words
Manage The Words
Search The Words
Answer The Quiz
Manage The Quiz
Mobile user
Admin
Database
Request The Words
29. Solution Complexity (Verbs Rules)
If Topic is فعل
AND char is equal to 3
AND category is مضارع فعل
THEN Add ي at the beginning of the word
If Topic is فعل
AND char is equal to 3
AND category is االمر فعل
THEN Add ا at the beginning of the words
If Topic is فعل
AND char is equal to 3
AND category is ماضي فعل
THEN Grammar is the same as inputs
32. Solution Complexity (Nouns Rules)
If Topic is اسم
AND Category is مذكر
AND Category is مفرد
THEN Grammar is same as inputs
If Topic is اسم
AND Category is مؤنث
AND Category is مفرد
THEN Add ة at the end of the word
If Topic is اسم
AND Category is مذكر OR مؤنث
AND Category is مثنى
THEN Add ا and ن at the end of the word
33. Solution Complexity (Nouns Rules)
If Topic is اسم
AND Category is مذكر
AND Category is السالم مذكر جمع
THEN Add و and ن at the end of the words
If Topic is اسم
AND Category is مؤنث
AND Category is السالم مؤنث جمع
THEN Replace ة with ا and ت at the end of the word