SlideShare a Scribd company logo

NL to OCL via SBVR

•Download as PPT, PDF•
•
Imran Bajwa
Imran Bajwa

Natural Langauge to OCL Transforamtion via SBVR

Education
1 of 16
Translation of Natural Language Specification to OCL A PhD Research Work Presentation RSMG 2 Imran Sarwar Bajwa PhD Student Natural Language Processing Group [email_address]
Presentation Foot Steps ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],29 | April | 2010 | 16 Natural Language Processing Group School of Computer Science University of Birmingham, UK
1.1 Motivation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],29 | April | 2010 | 16 Natural Language Processing Group School of Computer Science University of Birmingham, UK
1.2 Background ,[object Object],[object Object],[object Object],[object Object],29 | April | 2010 | 16 Natural Language Processing Group School of Computer Science University of Birmingham, UK Basic steps of Natural language Translation NL Text ( Source String ) Lexical Analysis ( Tokenizing ) g Syntactic Analysis ( Parsing ) Semantic Analysis ( Meaning Extraction ) Output ( Target String ) Translation
1.2 Background ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],29 | April | 2010 | 16 Natural Language Processing Group School of Computer Science University of Birmingham, UK
1.2 Background ,[object Object],29 | April | 2010 | 16 Natural Language Processing Group School of Computer Science University of Birmingham, UK ,[object Object],[object Object],[object Object],Airport Flight * * departTime: Time arrivalTime: Time duration : Interval maxNrPassengers: Integer origin desti- nation name: String arriving Flights departing Flights 1 1
1.2 Background ,[object Object],29 | April | 2010 | 16 Natural Language Processing Group School of Computer Science University of Birmingham, UK ,[object Object],[object Object],[object Object]
1.2 Background ,[object Object],29 | April | 2010 | 16 Natural Language Processing Group School of Computer Science University of Birmingham, UK ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
1.2 Background ,[object Object],29 | April | 2010 | 16 Natural Language Processing Group School of Computer Science University of Birmingham, UK ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
1.2 Background ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],29 | April | 2010 | 16 Natural Language Processing Group School of Computer Science University of Birmingham, UK
1.3 Problem Description ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],29 | April | 2010 | 16 Natural Language Processing Group School of Computer Science University of Birmingham, UK
1.4 Research Objectives ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],29 | April | 2010 | 16 Natural Language Processing Group School of Computer Science University of Birmingham, UK
2.1 Related Work ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],29 | April | 2010 | 16 Natural Language Processing Group School of Computer Science University of Birmingham, UK
2.2 Proposed Solution 29 | April | 2010 | 16 Natural Language Processing Group School of Computer Science University of Birmingham, UK Natural Language Specifications Structured English based SBVR SBVR to OCL Translation Mapping NL to SBVR Rules Validated OCL code Designer UML Model SBVR Rules OCL Expression Application Scenario of Natural language to OCL transformation context driver inv: self.age >= 18 It is necessary that each driver must be at least 18 years old . A driver should be 18 years old.
3. Conclusion 29 | April | 2010 | 16 Natural Language Processing Group School of Computer Science University of Birmingham, UK Finally!! OCL can be made more adaptable and usable by providing a natural language e.g. English based user interface.
29 | April | 2010 | 16 Natural Language Processing Group School of Computer Science University of Birmingham, UK ,[object Object],[object Object]

Recommended

C# programming : Chapter One
C# programming : Chapter OneC# programming : Chapter One
C# programming : Chapter OneKhairi Aiman
 
Object Oriented Technologies
Object Oriented TechnologiesObject Oriented Technologies
Object Oriented TechnologiesTushar B Kute
 
BERT: Bidirectional Encoder Representations from Transformers
BERT: Bidirectional Encoder Representations from TransformersBERT: Bidirectional Encoder Representations from Transformers
BERT: Bidirectional Encoder Representations from TransformersLiangqun Lu
 
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
BERT: Pre-training of Deep Bidirectional Transformers for Language UnderstandingBERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
BERT: Pre-training of Deep Bidirectional Transformers for Language UnderstandingMinh Pham
 
End-to-end sequence labeling via bi-directional LSTM-CNNs-CRF
End-to-end sequence labeling via bi-directional LSTM-CNNs-CRFEnd-to-end sequence labeling via bi-directional LSTM-CNNs-CRF
End-to-end sequence labeling via bi-directional LSTM-CNNs-CRFJayavardhan Reddy Peddamail
 
[Paper review] BERT
[Paper review] BERT[Paper review] BERT
[Paper review] BERTJEE HYUN PARK
 
A Feature-Based Model for Nested Named-Entity Recognition at VLSP-2018 NER Ev...
A Feature-Based Model for Nested Named-Entity Recognition at VLSP-2018 NER Ev...A Feature-Based Model for Nested Named-Entity Recognition at VLSP-2018 NER Ev...
A Feature-Based Model for Nested Named-Entity Recognition at VLSP-2018 NER Ev...Minh Pham
 
Bert
BertBert
BertAbdallah Bashir
 

Recommended

C# programming : Chapter One
C# programming : Chapter OneC# programming : Chapter One
C# programming : Chapter OneKhairi Aiman
 
Object Oriented Technologies
Object Oriented TechnologiesObject Oriented Technologies
Object Oriented TechnologiesTushar B Kute
 
BERT: Bidirectional Encoder Representations from Transformers
BERT: Bidirectional Encoder Representations from TransformersBERT: Bidirectional Encoder Representations from Transformers
BERT: Bidirectional Encoder Representations from TransformersLiangqun Lu
 
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
BERT: Pre-training of Deep Bidirectional Transformers for Language UnderstandingBERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
BERT: Pre-training of Deep Bidirectional Transformers for Language UnderstandingMinh Pham
 
End-to-end sequence labeling via bi-directional LSTM-CNNs-CRF
End-to-end sequence labeling via bi-directional LSTM-CNNs-CRFEnd-to-end sequence labeling via bi-directional LSTM-CNNs-CRF
End-to-end sequence labeling via bi-directional LSTM-CNNs-CRFJayavardhan Reddy Peddamail
 
[Paper review] BERT
[Paper review] BERT[Paper review] BERT
[Paper review] BERTJEE HYUN PARK
 
A Feature-Based Model for Nested Named-Entity Recognition at VLSP-2018 NER Ev...
A Feature-Based Model for Nested Named-Entity Recognition at VLSP-2018 NER Ev...A Feature-Based Model for Nested Named-Entity Recognition at VLSP-2018 NER Ev...
A Feature-Based Model for Nested Named-Entity Recognition at VLSP-2018 NER Ev...Minh Pham
 
Bert
BertBert
BertAbdallah Bashir
 
Polymorphism
PolymorphismPolymorphism
PolymorphismAmir Ali
 
Rahul Saini, BCA 2nd Year
Rahul Saini, BCA 2nd YearRahul Saini, BCA 2nd Year
Rahul Saini, BCA 2nd Yeardezyneecole
 
Study_of_Sequence_labeling_Systems
Study_of_Sequence_labeling_SystemsStudy_of_Sequence_labeling_Systems
Study_of_Sequence_labeling_SystemsJayavardhan Reddy Peddamail
 
Kajal Gaharwal,BCA 2nd Year
Kajal Gaharwal,BCA 2nd YearKajal Gaharwal,BCA 2nd Year
Kajal Gaharwal,BCA 2nd Yeardezyneecole
 
Polymorphism and its types
Polymorphism and its typesPolymorphism and its types
Polymorphism and its typesSuraj Bora
 
Pooja Sharma ,BCA 2nd Year
Pooja Sharma ,BCA 2nd YearPooja Sharma ,BCA 2nd Year
Pooja Sharma ,BCA 2nd Yeardezyneecole
 
Daksh Sharma ,BCA 2nd Year
Daksh Sharma ,BCA 2nd YearDaksh Sharma ,BCA 2nd Year
Daksh Sharma ,BCA 2nd Yeardezyneecole
 
7. Trevor Cohn (usfd) Statistical Machine Translation
7. Trevor Cohn (usfd) Statistical Machine Translation7. Trevor Cohn (usfd) Statistical Machine Translation
7. Trevor Cohn (usfd) Statistical Machine TranslationRIILP
 
Doppl development iteration #6
Doppl development iteration #6Doppl development iteration #6
Doppl development iteration #6Diego Perini
 
Tiancheng Zhao - 2017 - Learning Discourse-level Diversity for Neural Dialog...
Tiancheng Zhao - 2017 - Learning Discourse-level Diversity for Neural Dialog...Tiancheng Zhao - 2017 - Learning Discourse-level Diversity for Neural Dialog...
Tiancheng Zhao - 2017 - Learning Discourse-level Diversity for Neural Dialog...Association for Computational Linguistics
 
Pre trained language model
Pre trained language modelPre trained language model
Pre trained language modelJiWenKim
 
Language Model (D3L1 Deep Learning for Speech and Language UPC 2017)
Language Model (D3L1 Deep Learning for Speech and Language UPC 2017)Language Model (D3L1 Deep Learning for Speech and Language UPC 2017)
Language Model (D3L1 Deep Learning for Speech and Language UPC 2017)Universitat Politècnica de Catalunya
 
5. manuel arcedillo & juanjo arevalillo (hermes) translation memories
5. manuel arcedillo & juanjo arevalillo (hermes) translation memories5. manuel arcedillo & juanjo arevalillo (hermes) translation memories
5. manuel arcedillo & juanjo arevalillo (hermes) translation memoriesRIILP
 
Electra
ElectraElectra
ElectraSan Kim
 
Pinkle Makhijani ,BCA 2nd Year
Pinkle Makhijani ,BCA 2nd YearPinkle Makhijani ,BCA 2nd Year
Pinkle Makhijani ,BCA 2nd Yeardezyneecole
 
Object oriented programming
Object oriented programmingObject oriented programming
Object oriented programmingsana younas
 
Harendra Singh Rawat,BCA 2nd Year
Harendra Singh Rawat,BCA 2nd YearHarendra Singh Rawat,BCA 2nd Year
Harendra Singh Rawat,BCA 2nd Yeardezyneecole
 
From UML/OCL to natural language (using SBVR as pivot)
From UML/OCL to natural language (using SBVR as pivot)From UML/OCL to natural language (using SBVR as pivot)
From UML/OCL to natural language (using SBVR as pivot)Jordi Cabot
 
Farhaan Ahmed, BCA 2nd Year
Farhaan Ahmed, BCA 2nd YearFarhaan Ahmed, BCA 2nd Year
Farhaan Ahmed, BCA 2nd Yeardezyneecole
 
682177910 597183
682177910 597183682177910 597183
682177910 597183smumbahelp
 
NL to OCL Transformation (EDOC 2010)
NL to OCL Transformation (EDOC 2010)NL to OCL Transformation (EDOC 2010)
NL to OCL Transformation (EDOC 2010)IT Industry
 
An Efficient Approach to Produce Source Code by Interpreting Algorithm
An Efficient Approach to Produce Source Code by Interpreting AlgorithmAn Efficient Approach to Produce Source Code by Interpreting Algorithm
An Efficient Approach to Produce Source Code by Interpreting AlgorithmIRJET Journal
 

More Related Content

What's hot

Polymorphism
PolymorphismPolymorphism
PolymorphismAmir Ali
 
Rahul Saini, BCA 2nd Year
Rahul Saini, BCA 2nd YearRahul Saini, BCA 2nd Year
Rahul Saini, BCA 2nd Yeardezyneecole
 
Kajal Gaharwal,BCA 2nd Year
Kajal Gaharwal,BCA 2nd YearKajal Gaharwal,BCA 2nd Year
Kajal Gaharwal,BCA 2nd Yeardezyneecole
 
Polymorphism and its types
Polymorphism and its typesPolymorphism and its types
Polymorphism and its typesSuraj Bora
 
Pooja Sharma ,BCA 2nd Year
Pooja Sharma ,BCA 2nd YearPooja Sharma ,BCA 2nd Year
Pooja Sharma ,BCA 2nd Yeardezyneecole
 
Daksh Sharma ,BCA 2nd Year
Daksh Sharma ,BCA 2nd YearDaksh Sharma ,BCA 2nd Year
Daksh Sharma ,BCA 2nd Yeardezyneecole
 
7. Trevor Cohn (usfd) Statistical Machine Translation
7. Trevor Cohn (usfd) Statistical Machine Translation7. Trevor Cohn (usfd) Statistical Machine Translation
7. Trevor Cohn (usfd) Statistical Machine TranslationRIILP
 
Doppl development iteration #6
Doppl development iteration #6Doppl development iteration #6
Doppl development iteration #6Diego Perini
 
Tiancheng Zhao - 2017 - Learning Discourse-level Diversity for Neural Dialog...
Tiancheng Zhao - 2017 - Learning Discourse-level Diversity for Neural Dialog...Tiancheng Zhao - 2017 - Learning Discourse-level Diversity for Neural Dialog...
Tiancheng Zhao - 2017 - Learning Discourse-level Diversity for Neural Dialog...Association for Computational Linguistics
 
Pre trained language model
Pre trained language modelPre trained language model
Pre trained language modelJiWenKim
 
Language Model (D3L1 Deep Learning for Speech and Language UPC 2017)
Language Model (D3L1 Deep Learning for Speech and Language UPC 2017)Language Model (D3L1 Deep Learning for Speech and Language UPC 2017)
Language Model (D3L1 Deep Learning for Speech and Language UPC 2017)Universitat Politècnica de Catalunya
 
5. manuel arcedillo & juanjo arevalillo (hermes) translation memories
5. manuel arcedillo & juanjo arevalillo (hermes) translation memories5. manuel arcedillo & juanjo arevalillo (hermes) translation memories
5. manuel arcedillo & juanjo arevalillo (hermes) translation memoriesRIILP
 
Pinkle Makhijani ,BCA 2nd Year
Pinkle Makhijani ,BCA 2nd YearPinkle Makhijani ,BCA 2nd Year
Pinkle Makhijani ,BCA 2nd Yeardezyneecole
 
Object oriented programming
Object oriented programmingObject oriented programming
Object oriented programmingsana younas
 
Harendra Singh Rawat,BCA 2nd Year
Harendra Singh Rawat,BCA 2nd YearHarendra Singh Rawat,BCA 2nd Year
Harendra Singh Rawat,BCA 2nd Yeardezyneecole
 
From UML/OCL to natural language (using SBVR as pivot)
From UML/OCL to natural language (using SBVR as pivot)From UML/OCL to natural language (using SBVR as pivot)
From UML/OCL to natural language (using SBVR as pivot)Jordi Cabot
 
Farhaan Ahmed, BCA 2nd Year
Farhaan Ahmed, BCA 2nd YearFarhaan Ahmed, BCA 2nd Year
Farhaan Ahmed, BCA 2nd Yeardezyneecole
 
682177910 597183
682177910 597183682177910 597183
682177910 597183smumbahelp
 

What's hot (20)

Polymorphism
PolymorphismPolymorphism
Polymorphism
 
Rahul Saini, BCA 2nd Year
Rahul Saini, BCA 2nd YearRahul Saini, BCA 2nd Year
Rahul Saini, BCA 2nd Year
 
Study_of_Sequence_labeling_Systems
Study_of_Sequence_labeling_SystemsStudy_of_Sequence_labeling_Systems
Study_of_Sequence_labeling_Systems
 
Kajal Gaharwal,BCA 2nd Year
Kajal Gaharwal,BCA 2nd YearKajal Gaharwal,BCA 2nd Year
Kajal Gaharwal,BCA 2nd Year
 
Polymorphism and its types
Polymorphism and its typesPolymorphism and its types
Polymorphism and its types
 
Pooja Sharma ,BCA 2nd Year
Pooja Sharma ,BCA 2nd YearPooja Sharma ,BCA 2nd Year
Pooja Sharma ,BCA 2nd Year
 
Daksh Sharma ,BCA 2nd Year
Daksh Sharma ,BCA 2nd YearDaksh Sharma ,BCA 2nd Year
Daksh Sharma ,BCA 2nd Year
 
7. Trevor Cohn (usfd) Statistical Machine Translation
7. Trevor Cohn (usfd) Statistical Machine Translation7. Trevor Cohn (usfd) Statistical Machine Translation
7. Trevor Cohn (usfd) Statistical Machine Translation
 
Doppl development iteration #6
Doppl development iteration #6Doppl development iteration #6
Doppl development iteration #6
 
Tiancheng Zhao - 2017 - Learning Discourse-level Diversity for Neural Dialog...
Tiancheng Zhao - 2017 - Learning Discourse-level Diversity for Neural Dialog...Tiancheng Zhao - 2017 - Learning Discourse-level Diversity for Neural Dialog...
Tiancheng Zhao - 2017 - Learning Discourse-level Diversity for Neural Dialog...
 
Pre trained language model
Pre trained language modelPre trained language model
Pre trained language model
 
Language Model (D3L1 Deep Learning for Speech and Language UPC 2017)
Language Model (D3L1 Deep Learning for Speech and Language UPC 2017)Language Model (D3L1 Deep Learning for Speech and Language UPC 2017)
Language Model (D3L1 Deep Learning for Speech and Language UPC 2017)
 
5. manuel arcedillo & juanjo arevalillo (hermes) translation memories
5. manuel arcedillo & juanjo arevalillo (hermes) translation memories5. manuel arcedillo & juanjo arevalillo (hermes) translation memories
5. manuel arcedillo & juanjo arevalillo (hermes) translation memories
 
Electra
ElectraElectra
Electra
 
Pinkle Makhijani ,BCA 2nd Year
Pinkle Makhijani ,BCA 2nd YearPinkle Makhijani ,BCA 2nd Year
Pinkle Makhijani ,BCA 2nd Year
 
Object oriented programming
Object oriented programmingObject oriented programming
Object oriented programming
 
Harendra Singh Rawat,BCA 2nd Year
Harendra Singh Rawat,BCA 2nd YearHarendra Singh Rawat,BCA 2nd Year
Harendra Singh Rawat,BCA 2nd Year
 
From UML/OCL to natural language (using SBVR as pivot)
From UML/OCL to natural language (using SBVR as pivot)From UML/OCL to natural language (using SBVR as pivot)
From UML/OCL to natural language (using SBVR as pivot)
 
Farhaan Ahmed, BCA 2nd Year
Farhaan Ahmed, BCA 2nd YearFarhaan Ahmed, BCA 2nd Year
Farhaan Ahmed, BCA 2nd Year
 
682177910 597183
682177910 597183682177910 597183
682177910 597183
 

Similar to NL to OCL via SBVR

NL to OCL Transformation (EDOC 2010)
NL to OCL Transformation (EDOC 2010)NL to OCL Transformation (EDOC 2010)
NL to OCL Transformation (EDOC 2010)IT Industry
 
An Efficient Approach to Produce Source Code by Interpreting Algorithm
An Efficient Approach to Produce Source Code by Interpreting AlgorithmAn Efficient Approach to Produce Source Code by Interpreting Algorithm
An Efficient Approach to Produce Source Code by Interpreting AlgorithmIRJET Journal
 
19BCE1367_Capstone_Review 2_Final.pdf
19BCE1367_Capstone_Review 2_Final.pdf19BCE1367_Capstone_Review 2_Final.pdf
19BCE1367_Capstone_Review 2_Final.pdfAnjali Thacker
 
Learning to Generate Pseudo-code from Source Code using Statistical Machine T...
Learning to Generate Pseudo-code from Source Code using Statistical Machine T...Learning to Generate Pseudo-code from Source Code using Statistical Machine T...
Learning to Generate Pseudo-code from Source Code using Statistical Machine T...Yusuke Oda
 
Transformer Seq2Sqe Models: Concepts, Trends & Limitations (DLI)
Transformer Seq2Sqe Models: Concepts, Trends & Limitations (DLI)Transformer Seq2Sqe Models: Concepts, Trends & Limitations (DLI)
Transformer Seq2Sqe Models: Concepts, Trends & Limitations (DLI)Deep Learning Italia
 
Automatic Grammatical Error Correction for ESL-Learners by SMT - Getting it r...
Automatic Grammatical Error Correction for ESL-Learners by SMT - Getting it r...Automatic Grammatical Error Correction for ESL-Learners by SMT - Getting it r...
Automatic Grammatical Error Correction for ESL-Learners by SMT - Getting it r...Marcin Junczys-Dowmunt
 
intro-slides.pdf very important for computer science students
intro-slides.pdf very important for computer science studentsintro-slides.pdf very important for computer science students
intro-slides.pdf very important for computer science studentssairevanth504
 
C language by Dr. D. R. Gholkar
C language by Dr. D. R. GholkarC language by Dr. D. R. Gholkar
C language by Dr. D. R. GholkarPRAVIN GHOLKAR
 
Introduction to OpenSees by Frank McKenna
Introduction to OpenSees by Frank McKennaIntroduction to OpenSees by Frank McKenna
Introduction to OpenSees by Frank McKennaopenseesdays
 
Offline Reinforcement Learning for Informal Summarization in Online Domains.pdf
Offline Reinforcement Learning for Informal Summarization in Online Domains.pdfOffline Reinforcement Learning for Informal Summarization in Online Domains.pdf
Offline Reinforcement Learning for Informal Summarization in Online Domains.pdfPo-Chuan Chen
 
Joint Copying and Restricted Generation for Paraphrase
Joint Copying and Restricted Generation for ParaphraseJoint Copying and Restricted Generation for Paraphrase
Joint Copying and Restricted Generation for ParaphraseMasahiro Kaneko
 
MATLAB programming for engineers
MATLAB programming for engineersMATLAB programming for engineers
MATLAB programming for engineersJARossiter
 
Cost estimation using cocomo model
Cost estimation using cocomo modelCost estimation using cocomo model
Cost estimation using cocomo modelNitesh Bichwani
 
Automated Context-based Question-Distractor Generation using Extractive Summa...
Automated Context-based Question-Distractor Generation using Extractive Summa...Automated Context-based Question-Distractor Generation using Extractive Summa...
Automated Context-based Question-Distractor Generation using Extractive Summa...IRJET Journal
 

Similar to NL to OCL via SBVR (20)

NL to OCL Transformation (EDOC 2010)
NL to OCL Transformation (EDOC 2010)NL to OCL Transformation (EDOC 2010)
NL to OCL Transformation (EDOC 2010)
 
An Efficient Approach to Produce Source Code by Interpreting Algorithm
An Efficient Approach to Produce Source Code by Interpreting AlgorithmAn Efficient Approach to Produce Source Code by Interpreting Algorithm
An Efficient Approach to Produce Source Code by Interpreting Algorithm
 
OCL in EMF
OCL in EMFOCL in EMF
OCL in EMF
 
Objc
ObjcObjc
Objc
 
19BCE1367_Capstone_Review 2_Final.pdf
19BCE1367_Capstone_Review 2_Final.pdf19BCE1367_Capstone_Review 2_Final.pdf
19BCE1367_Capstone_Review 2_Final.pdf
 
Learning to Generate Pseudo-code from Source Code using Statistical Machine T...
Learning to Generate Pseudo-code from Source Code using Statistical Machine T...Learning to Generate Pseudo-code from Source Code using Statistical Machine T...
Learning to Generate Pseudo-code from Source Code using Statistical Machine T...
 
Transformer Seq2Sqe Models: Concepts, Trends & Limitations (DLI)
Transformer Seq2Sqe Models: Concepts, Trends & Limitations (DLI)Transformer Seq2Sqe Models: Concepts, Trends & Limitations (DLI)
Transformer Seq2Sqe Models: Concepts, Trends & Limitations (DLI)
 
Automatic Grammatical Error Correction for ESL-Learners by SMT - Getting it r...
Automatic Grammatical Error Correction for ESL-Learners by SMT - Getting it r...Automatic Grammatical Error Correction for ESL-Learners by SMT - Getting it r...
Automatic Grammatical Error Correction for ESL-Learners by SMT - Getting it r...
 
intro-slides.pdf very important for computer science students
intro-slides.pdf very important for computer science studentsintro-slides.pdf very important for computer science students
intro-slides.pdf very important for computer science students
 
C language by Dr. D. R. Gholkar
C language by Dr. D. R. GholkarC language by Dr. D. R. Gholkar
C language by Dr. D. R. Gholkar
 
Object-Oriented Programming Using C++
Object-Oriented Programming Using C++Object-Oriented Programming Using C++
Object-Oriented Programming Using C++
 
Introduction to OpenSees by Frank McKenna
Introduction to OpenSees by Frank McKennaIntroduction to OpenSees by Frank McKenna
Introduction to OpenSees by Frank McKenna
 
Offline Reinforcement Learning for Informal Summarization in Online Domains.pdf
Offline Reinforcement Learning for Informal Summarization in Online Domains.pdfOffline Reinforcement Learning for Informal Summarization in Online Domains.pdf
Offline Reinforcement Learning for Informal Summarization in Online Domains.pdf
 
OODPunit1.pdf
OODPunit1.pdfOODPunit1.pdf
OODPunit1.pdf
 
ICT C++
ICT C++ ICT C++
ICT C++
 
Joint Copying and Restricted Generation for Paraphrase
Joint Copying and Restricted Generation for ParaphraseJoint Copying and Restricted Generation for Paraphrase
Joint Copying and Restricted Generation for Paraphrase
 
MATLAB programming for engineers
MATLAB programming for engineersMATLAB programming for engineers
MATLAB programming for engineers
 
Cost estimation using cocomo model
Cost estimation using cocomo modelCost estimation using cocomo model
Cost estimation using cocomo model
 
Chapter 1
Chapter 1Chapter 1
Chapter 1
 
Automated Context-based Question-Distractor Generation using Extractive Summa...
Automated Context-based Question-Distractor Generation using Extractive Summa...Automated Context-based Question-Distractor Generation using Extractive Summa...
Automated Context-based Question-Distractor Generation using Extractive Summa...
 

Recently uploaded

Science 7 Quarter 4 Module 2: Natural Resources.pptx
Science 7 Quarter 4 Module 2: Natural Resources.pptxScience 7 Quarter 4 Module 2: Natural Resources.pptx
Science 7 Quarter 4 Module 2: Natural Resources.pptxMaryGraceBautista27
 
ISYU TUNGKOL SA SEKSWLADIDA (ISSUE ABOUT SEXUALITY
ISYU TUNGKOL SA SEKSWLADIDA (ISSUE ABOUT SEXUALITYISYU TUNGKOL SA SEKSWLADIDA (ISSUE ABOUT SEXUALITY
ISYU TUNGKOL SA SEKSWLADIDA (ISSUE ABOUT SEXUALITYKayeClaireEstoconing
 
Full Stack Web Development Course for Beginners
Full Stack Web Development Course for BeginnersFull Stack Web Development Course for Beginners
Full Stack Web Development Course for BeginnersSabitha Banu
 
MULTIDISCIPLINRY NATURE OF THE ENVIRONMENTAL STUDIES.pptx
MULTIDISCIPLINRY NATURE OF THE ENVIRONMENTAL STUDIES.pptxMULTIDISCIPLINRY NATURE OF THE ENVIRONMENTAL STUDIES.pptx
MULTIDISCIPLINRY NATURE OF THE ENVIRONMENTAL STUDIES.pptxAnupkumar Sharma
 
INTRODUCTION TO CATHOLIC CHRISTOLOGY.pptx
INTRODUCTION TO CATHOLIC CHRISTOLOGY.pptxINTRODUCTION TO CATHOLIC CHRISTOLOGY.pptx
INTRODUCTION TO CATHOLIC CHRISTOLOGY.pptxHumphrey A Beña
 
ANG SEKTOR NG agrikultura.pptx QUARTER 4
ANG SEKTOR NG agrikultura.pptx QUARTER 4ANG SEKTOR NG agrikultura.pptx QUARTER 4
ANG SEKTOR NG agrikultura.pptx QUARTER 4MiaBumagat1
 
call girls in Kamla Market (DELHI) 🔝 >༒9953330565🔝 genuine Escort Service 🔝✔️✔️
call girls in Kamla Market (DELHI) 🔝 >༒9953330565🔝 genuine Escort Service 🔝✔️✔️call girls in Kamla Market (DELHI) 🔝 >༒9953330565🔝 genuine Escort Service 🔝✔️✔️
call girls in Kamla Market (DELHI) 🔝 >༒9953330565🔝 genuine Escort Service 🔝✔️✔️9953056974 Low Rate Call Girls In Saket, Delhi NCR
 
Q4 English4 Week3 PPT Melcnmg-based.pptx
Q4 English4 Week3 PPT Melcnmg-based.pptxQ4 English4 Week3 PPT Melcnmg-based.pptx
Q4 English4 Week3 PPT Melcnmg-based.pptxnelietumpap1
 
USPS® Forced Meter Migration - How to Know if Your Postage Meter Will Soon be...
USPS® Forced Meter Migration - How to Know if Your Postage Meter Will Soon be...USPS® Forced Meter Migration - How to Know if Your Postage Meter Will Soon be...
USPS® Forced Meter Migration - How to Know if Your Postage Meter Will Soon be...Postal Advocate Inc.
 
Influencing policy (training slides from Fast Track Impact)
Influencing policy (training slides from Fast Track Impact)Influencing policy (training slides from Fast Track Impact)
Influencing policy (training slides from Fast Track Impact)Mark Reed
 
Like-prefer-love -hate+verb+ing & silent letters & citizenship text.pdf
Like-prefer-love -hate+verb+ing & silent letters & citizenship text.pdfLike-prefer-love -hate+verb+ing & silent letters & citizenship text.pdf
Like-prefer-love -hate+verb+ing & silent letters & citizenship text.pdfMr Bounab Samir
 
How to do quick user assign in kanban in Odoo 17 ERP
How to do quick user assign in kanban in Odoo 17 ERPHow to do quick user assign in kanban in Odoo 17 ERP
How to do quick user assign in kanban in Odoo 17 ERPCeline George
 
Visit to a blind student's school🧑‍🦯🧑‍🦯(community medicine)
Visit to a blind student's school🧑‍🦯🧑‍🦯(community medicine)Visit to a blind student's school🧑‍🦯🧑‍🦯(community medicine)
Visit to a blind student's school🧑‍🦯🧑‍🦯(community medicine)lakshayb543
 
ECONOMIC CONTEXT - LONG FORM TV DRAMA - PPT
ECONOMIC CONTEXT - LONG FORM TV DRAMA - PPTECONOMIC CONTEXT - LONG FORM TV DRAMA - PPT
ECONOMIC CONTEXT - LONG FORM TV DRAMA - PPTiammrhaywood
 
Choosing the Right CBSE School A Comprehensive Guide for Parents
Choosing the Right CBSE School A Comprehensive Guide for ParentsChoosing the Right CBSE School A Comprehensive Guide for Parents
Choosing the Right CBSE School A Comprehensive Guide for Parentsnavabharathschool99
 
ACC 2024 Chronicles. Cardiology. Exam.pdf
ACC 2024 Chronicles. Cardiology. Exam.pdfACC 2024 Chronicles. Cardiology. Exam.pdf
ACC 2024 Chronicles. Cardiology. Exam.pdfSpandanaRallapalli
 

Recently uploaded (20)

Science 7 Quarter 4 Module 2: Natural Resources.pptx
Science 7 Quarter 4 Module 2: Natural Resources.pptxScience 7 Quarter 4 Module 2: Natural Resources.pptx
Science 7 Quarter 4 Module 2: Natural Resources.pptx
 
ISYU TUNGKOL SA SEKSWLADIDA (ISSUE ABOUT SEXUALITY
ISYU TUNGKOL SA SEKSWLADIDA (ISSUE ABOUT SEXUALITYISYU TUNGKOL SA SEKSWLADIDA (ISSUE ABOUT SEXUALITY
ISYU TUNGKOL SA SEKSWLADIDA (ISSUE ABOUT SEXUALITY
 
Full Stack Web Development Course for Beginners
Full Stack Web Development Course for BeginnersFull Stack Web Development Course for Beginners
Full Stack Web Development Course for Beginners
 
MULTIDISCIPLINRY NATURE OF THE ENVIRONMENTAL STUDIES.pptx
MULTIDISCIPLINRY NATURE OF THE ENVIRONMENTAL STUDIES.pptxMULTIDISCIPLINRY NATURE OF THE ENVIRONMENTAL STUDIES.pptx
MULTIDISCIPLINRY NATURE OF THE ENVIRONMENTAL STUDIES.pptx
 
INTRODUCTION TO CATHOLIC CHRISTOLOGY.pptx
INTRODUCTION TO CATHOLIC CHRISTOLOGY.pptxINTRODUCTION TO CATHOLIC CHRISTOLOGY.pptx
INTRODUCTION TO CATHOLIC CHRISTOLOGY.pptx
 
ANG SEKTOR NG agrikultura.pptx QUARTER 4
ANG SEKTOR NG agrikultura.pptx QUARTER 4ANG SEKTOR NG agrikultura.pptx QUARTER 4
ANG SEKTOR NG agrikultura.pptx QUARTER 4
 
YOUVE_GOT_EMAIL_PRELIMS_EL_DORADO_2024.pptx
YOUVE_GOT_EMAIL_PRELIMS_EL_DORADO_2024.pptxYOUVE_GOT_EMAIL_PRELIMS_EL_DORADO_2024.pptx
YOUVE_GOT_EMAIL_PRELIMS_EL_DORADO_2024.pptx
 
call girls in Kamla Market (DELHI) 🔝 >༒9953330565🔝 genuine Escort Service 🔝✔️✔️
call girls in Kamla Market (DELHI) 🔝 >༒9953330565🔝 genuine Escort Service 🔝✔️✔️call girls in Kamla Market (DELHI) 🔝 >༒9953330565🔝 genuine Escort Service 🔝✔️✔️
call girls in Kamla Market (DELHI) 🔝 >༒9953330565🔝 genuine Escort Service 🔝✔️✔️
 
Q4 English4 Week3 PPT Melcnmg-based.pptx
Q4 English4 Week3 PPT Melcnmg-based.pptxQ4 English4 Week3 PPT Melcnmg-based.pptx
Q4 English4 Week3 PPT Melcnmg-based.pptx
 
USPS® Forced Meter Migration - How to Know if Your Postage Meter Will Soon be...
USPS® Forced Meter Migration - How to Know if Your Postage Meter Will Soon be...USPS® Forced Meter Migration - How to Know if Your Postage Meter Will Soon be...
USPS® Forced Meter Migration - How to Know if Your Postage Meter Will Soon be...
 
Influencing policy (training slides from Fast Track Impact)
Influencing policy (training slides from Fast Track Impact)Influencing policy (training slides from Fast Track Impact)
Influencing policy (training slides from Fast Track Impact)
 
Like-prefer-love -hate+verb+ing & silent letters & citizenship text.pdf
Like-prefer-love -hate+verb+ing & silent letters & citizenship text.pdfLike-prefer-love -hate+verb+ing & silent letters & citizenship text.pdf
Like-prefer-love -hate+verb+ing & silent letters & citizenship text.pdf
 
How to do quick user assign in kanban in Odoo 17 ERP
How to do quick user assign in kanban in Odoo 17 ERPHow to do quick user assign in kanban in Odoo 17 ERP
How to do quick user assign in kanban in Odoo 17 ERP
 
LEFT_ON_C'N_ PRELIMS_EL_DORADO_2024.pptx
LEFT_ON_C'N_ PRELIMS_EL_DORADO_2024.pptxLEFT_ON_C'N_ PRELIMS_EL_DORADO_2024.pptx
LEFT_ON_C'N_ PRELIMS_EL_DORADO_2024.pptx
 
TataKelola dan KamSiber Kecerdasan Buatan v022.pdf
TataKelola dan KamSiber Kecerdasan Buatan v022.pdfTataKelola dan KamSiber Kecerdasan Buatan v022.pdf
TataKelola dan KamSiber Kecerdasan Buatan v022.pdf
 
OS-operating systems- ch04 (Threads) ...
OS-operating systems- ch04 (Threads) ...OS-operating systems- ch04 (Threads) ...
OS-operating systems- ch04 (Threads) ...
 
Visit to a blind student's school🧑‍🦯🧑‍🦯(community medicine)
Visit to a blind student's school🧑‍🦯🧑‍🦯(community medicine)Visit to a blind student's school🧑‍🦯🧑‍🦯(community medicine)
Visit to a blind student's school🧑‍🦯🧑‍🦯(community medicine)
 
ECONOMIC CONTEXT - LONG FORM TV DRAMA - PPT
ECONOMIC CONTEXT - LONG FORM TV DRAMA - PPTECONOMIC CONTEXT - LONG FORM TV DRAMA - PPT
ECONOMIC CONTEXT - LONG FORM TV DRAMA - PPT
 
Choosing the Right CBSE School A Comprehensive Guide for Parents
Choosing the Right CBSE School A Comprehensive Guide for ParentsChoosing the Right CBSE School A Comprehensive Guide for Parents
Choosing the Right CBSE School A Comprehensive Guide for Parents
 
ACC 2024 Chronicles. Cardiology. Exam.pdf
ACC 2024 Chronicles. Cardiology. Exam.pdfACC 2024 Chronicles. Cardiology. Exam.pdf
ACC 2024 Chronicles. Cardiology. Exam.pdf
 

NL to OCL via SBVR

  • 1. Translation of Natural Language Specification to OCL A PhD Research Work Presentation RSMG 2 Imran Sarwar Bajwa PhD Student Natural Language Processing Group [email_address]
  • 2.
  • 3.
  • 4.
  • 5.
  • 6.
  • 7.
  • 8.
  • 9.
  • 10.
  • 11.
  • 12.
  • 13.
  • 14. 2.2 Proposed Solution 29 | April | 2010 | 16 Natural Language Processing Group School of Computer Science University of Birmingham, UK Natural Language Specifications Structured English based SBVR SBVR to OCL Translation Mapping NL to SBVR Rules Validated OCL code Designer UML Model SBVR Rules OCL Expression Application Scenario of Natural language to OCL transformation context driver inv: self.age >= 18 It is necessary that each driver must be at least 18 years old . A driver should be 18 years old.
  • 15. 3. Conclusion 29 | April | 2010 | 16 Natural Language Processing Group School of Computer Science University of Birmingham, UK Finally!! OCL can be made more adaptable and usable by providing a natural language e.g. English based user interface.
  • 16.