SlideShare a Scribd company logo
1 of 5
Download to read offline
IOSR Journal of Computer Engineering (IOSR-JCE)
e-ISSN: 2278-0661, p- ISSN: 2278-8727Volume 15, Issue 6 (Nov. - Dec. 2013), PP 54-58
www.iosrjournals.org
www.iosrjournals.org 54 | Page
Computer aided environment for drawing (to set) fill in the blank
from given paragraph.
Sheetal Rakangor and Dr. Yogesh Ghodasara
Lecturer, Depatment of Computer Science, Saurashtra University, Rajkot 360001
Associate Professor, College of Agricultural Information Technology, Anand 388110
Abstract: In this paper, we present Development of computer aided environment for drawing (to set) fill in the
blanks that can generate for given paragraph. The System finds fill in the blanks, blanking key generates from
the selected statement. Syntactic and lexical features are used in this process. NLP parser is used, part of speech
taggers are applied on each of these sentences to encode necessary information.
We present our work in designing and implementing the system which generate the blanks. The System is
developed in Java using JDBC which is open source.
Keyword: Natural Language Processing NLP, Key selection, POS tagging, Sentence selection
I. Introduction
Now a day, increasing in population affects the number of students in every education stream. If we
compare the scenario before 10 to 15 years with the current scenario, the number of students in each stream
increases rapidly.
In any educational system, student has to appear for the examination to determine their performance and
depending upon their performance they will be promoted for the next year. To evaluate the performance of the
student different evaluation methods i.e. examination system are implemented. Fill in the blank questions where
one or more words are removed from a sentence/paragraph, are easy to evaluate. Preparing this question
manually required lots of time and efforts so with the help of this system teacher can automatically generate the
fill in the blanks question from given paragraph.
Some of the earlier attempts in generating fill in the blanks are given below:
One of the first automatic question generation systems proposed for supporting learning activities was
AUTOQUEST [2]. Used pattern matching approach to generate question from one of the sentences and grade
the student answer. The purpose of these questions is to help novices to learn English.
Automatic gaps fill question generation system by Manish Agrawal and prashant mannem [4] generates fill in
the blanks with options generated from text book without external resources except the document from text
book. Sentence Selection, Key Selection and distractor are selected from chapter only. POS tagger and typed
dependency is used for generating question.
TEDDCLOG [5] System take input as key and finds distractor from distributional thesaurus and find the
sentence having inputted key and does not have distractor. Then system presents the sentence with distractor for
approval, modification and rejection.
MCTI [6] system is semi automatic system consist following task Key Term Extraction, Source Clause
Selection, Transformation to stem and Distractor Selection. In this system first key Term Identification is done
than on that basis sentence And then Transformation of stem means convert the sentence into wh-phrase. And
last distractors are created.
Computer aided multiple choice test items [8] is created post-editing interface where term extraction, sentence
transformation, and for distractor selection Wordnet and corpus external resources were used. In this system
post editing is used and user friendly interface is used for modification of question. And provide list of multiple
distractor, user can select distractor’s from that list for MCQ generated by system.
Most of the system approaches use is to select key first, and then sentence is selected. We have given priority to
sentence and then from the important sentence key is extracted.
Computer aided environment for drawing (to set) fill in the blank from given paragraph.
www.iosrjournals.org 55 | Page
II. Model And Methodology
The following goals were set for the proposed computerized system for drawing question paper, from given
paragraph:
Fill in the blanks questions are generated from it in two stages: sentence selection and key selection Sentence
selection involves identifying sentences in the paragraph which can be used to generate question. These
sentences are then processed in the key selection stage to identify the key on which to ask the question.
Architecture of System
In the Architecture of the system, User will input the paragraph, through interactive user friendly editor.
Figure 1 : Architecture of System
In this approach user will enter the paragraph from the system. And extract the sentences and select the
important sentence from the given paragraph. Using certain features which are explained below, then key list is
generated from the important sentence selection. From the key list, potential key is extracted from system and
blank is generated from the given paragraph.
Sentence Selection
In Sentence Selection module goes through all sentences from given paragraph and used NLP Stanford parser
which parser the sentences and divided into small fragments called token. And from that token POS tagger is
used, which provides a representation of grammatical relations between words in a sentence. And extract a set of
features like,
Count number of Sentences: Paragraph entered count number of sentences from that paragraph.
Count number of words: Count number of words in the sentence. Short sentence generate unanswerable
question because short content and very long content might have enough content to make the question
generated.
Count number of nouns: Noun gives an idea about the sentences, if maximum number of noun in sentence
means potential key can be generated from that sentence and that sentence having good content which can
generate the key for fill in the blanks.
Superlative: Superlative degree defines exaggerated mode of expression or height of quality. Superlative are
typically formed with suffix –est (healthiest) or the word most, good, best are used. Sentence which contains
superlative degree can generate good fill in the blanks.
On the basis of these features important sentence will be extracted from the given paragraph.
Algorithm for sentence selection
1. Enter the paragraph P
2. Read the statements from the paragraph S.
3. Calculate number of sentences CtS.
4. Calculate number of words from each sentence CtW
5. For each CtNoun and CtSuper from S do
Select the sentence which contains superlative degree and then calculate maximum number of
nouns which contain superlative degree.
6. IF CtSuper and CtNoun from S then
Computer aided environment for drawing (to set) fill in the blank from given paragraph.
www.iosrjournals.org 56 | Page
SetenceSelected SS
ElseIF Max (CtNoun) from S then
SetenceSelected SS
If there is no superlative degree then select that sentence which having maximum number of
noun
Else
Without Noun and Superlative degree, blanks will not generated
EndIF
EndFor
7. Display SentenceSelected SS
Key Selection
Key selection is most important stage, to identify the key from important sentence to ask the question on.
Previous work in this area, [5] takes key as input and [6] select key on basis of regular expression on noun. Or
first search the key and then basic sentence is selected. Our key selection approach is divided into two stages.
Generate Potential keys from the statement and select Best Key form that key list [4].
First Stage: Potential keys are generated from selected sentences, POS tagger is used to identify the words and
there type. Suppose we need to generate key for noun then select total numbers of noun available in sentence
and list them, and if any noun word is repeated in that list, it would be removed from list [4].
Second Stage: Best keys are generated from the key-list, select the word from key-list and search that word in
paragraph. Count how many times that word has been used in paragraph. Select best key which has found in
selected sentence and noun repeated in paragraph maximum number of times.
In our system we have used Stanford parser through which POS tagger will use for inputted paragraph.
Algorithm for key selection
1. For Each Word from SS do
From selected sentence extract the potential key now suppose we need to generate key of noun then
extract the nouns from the sentence. And add them into the keylist.
KeyList =Select Noun from SS
BestKey = No of Occurrence of that key in SS and Height of that key in the syntactic tree Structure.
End For
2. Remove that BestKey from sentence SS and generate Fill in the blanks.
So this way system will generate the fill in the blanks from given paragraph, below figure shows the how system
works when user entered user enters the paragraph with example.
Computer aided environment for drawing (to set) fill in the blank from given paragraph.
www.iosrjournals.org 57 | Page
Figure 2: Process flow diagram of System
III. Evaluation And Result
The System has tested with number of small and simple paragraph. The table shows some of the
implementation of paragraphs, first sentence is selected on the basis of the features describes earlier and best
key is selected from the selected sentence and then fill in the blanks is generated from the given paragraph. And
Figure 3 shows screen short of the system how system generates the fill in the blanks. The system is developed
in Java using JDBC for generating fill in the blanks.
No. Entered Paragraph Selected Sentence Key
selected
Fill in the blank
generated.
1 Rajkot is clean City . In Rajkot there
are many beautiful places where we
can go for outing . Rajkot City is part
of Gujarat .
Rajkot City is part of
Gujarat
Rajkot ____________City is
part of Gujarat
2 Shrusti is the most sincere student in
the class . while soniya is the most
mischievous student. In fact Shrusti
and soniya are most regular student.
While kruti is the most beautiful
student. Overall my class is the best
of all.
In fact Shrusti and soniya
are most regular student.
Shrusti In fact _________ and
soniya are most regular
student
3 Sachin Ramesh Tendulkar born 24
April 1973. Sachin is a former Indian
cricketer widely acknowledged as the
greatest batsman of the modern.
Sachin took up cricket at the age of
eleven, made his Test debut against
Pakistan at the age of sixteen, and
went on to represent Mumbai
domestically and India internationally
for close to twenty-four years.
Sachin took up cricket at
the age of eleven, made his
Test debut against Pakistan
at the age of sixteen, and
went on to represent
Mumbai domestically and
India internationally for
close to twenty-four years.
Sachin ____________took up
cricket at the age of
eleven , made his Test
debut against Pakistan at
the age of sixteen , and
went on to represent
Mumbai domestically
and India internationally
for close to twenty-four
years
4 Butterflies are one of the most
beautiful and interesting creatures on
earth. Butterflies attractive, brilliant
colors catch interest immediately.
They have wings of varying shapes
and sizes, and some even appear to
Butterflies are one of the
most beautiful and
interesting creatures on
earth
Butterflies ___________ are one of
the most beautiful and
interesting creatures on
earth
Computer aided environment for drawing (to set) fill in the blank from given paragraph.
www.iosrjournals.org 58 | Page
have eyes on them.
5 The peacock is the national bird of
India. They have colourful feathers,
two legs and a small beak. They are
famous for their dance. When a
peacock dances it spreads its feathers
like a fan. It has a long shiny dark
blue neck. Peacocks are mostly found
in the fields they are very beautiful
birds. The females are known as
'Peahen. Their feathers are used for
making jackets, purses etc. We can
see them in a zoo.
The peacock is the national
bird of India
Peacock The ____________is the
national bird of India
Table :1 Example of Paragraphs tested in system.
Figure 3: A Sample Screen short of the system.
IV. Conclusion And Future Work
The system select important sentence from the given paragraph, and generate fill in the blanks question
on them. Syntactic features helped in quality of fill in the blanks generated. We look forward to generate large
number of blanks from the given paragraph as well as generate MCQS and True or False from given paragraph.
Evaluation of Single blank generated is done in Java with the help of Stanford parser.
References
[1] Pollock, M.J., Whittington, C.D., Doughty, G.F.: Evaluating the Costs and Benefits of Changing to CAA. Proceedings of the Fourth
International Computer Assisted Conference CAA, http://www.caaconference.com/. (2000).
[2] Wolfe, J.H.: Automatic question generation from text - an aid to independent study. SIGCUE Outlook 10(SI) (1976)
[3] Kunichika, H., Katayama, T., Hirashima, T., Takeuchi, A.: Automated question generation methods for intelligent english learning
systems and its evaluation, Proc. of ICCE01 (2001).
[4] Manish Agarwal and Prashanth Mannem : Automatic Gap-fill Question generation from text books
[5] Simon Smith, P.V.S Avinesh and Adam Kilgarriff. 2010.Gap-fill Tests for Language Learners: Corpus-Driven Item Generation .
[6] Nikiforos Karamanis, Le An Ha and Ruslan Mitkov: Generating Multiple-Choice Test Items from Medical Text:A Pilot Study
[7] Naveed Afzal and Viktor Pekar: Unsupervised Relation Extraction for Automatic Generation of Multiple-Choice Questions.
[8] RUSLAN MITKOV, LE AN HA and NIKIFOROS KARAMANIS: A computer-aided environment for generating multiple-choice
test items(2005)
[9] Le An Ha :Multiple-choice test item generation: A demo Vasile Rus, Brendan Wyse, Paul Piwek, Mihai Lintean, Svetlana
Stoyanchev and Cristian Moldovan:The First Question Generation Shared Task Evaluation Challenge (2010)
[10] Husam Ali Yllias Chali Sadid A. Hasan: Automatic Question Generation from Sentences(2010)
[11] Takuya Goto, Tomoko Kojiri, Toyohide Watanabe, Tomoharu Iwata, Takeshi Yamada : Automatic Generation System of
Multiple-Choice Cloze Questions and its Evaluation
[12] John Lee, Stephanie Seneff: Automatic Generation of Cloze Items for Prepositions (2007)
[13] Amisha Shingala, Rinku Chavda and Paresh Virparia : NATURAL LANGUAGE INTERFACE FOR STUDENT INFORMATION
SYSTEM (NLSIS). Prajna(journal) Vol.19 41 - 44 (2011)

More Related Content

What's hot

TOWARDS MAKING SENSE OF ONLINE REVIEWS BASED ON STATEMENT EXTRACTION
TOWARDS MAKING SENSE OF ONLINE REVIEWS BASED ON STATEMENT EXTRACTIONTOWARDS MAKING SENSE OF ONLINE REVIEWS BASED ON STATEMENT EXTRACTION
TOWARDS MAKING SENSE OF ONLINE REVIEWS BASED ON STATEMENT EXTRACTIONcscpconf
 
Detection of slang words in e data using semi supervised learning
Detection of slang words in e data using semi supervised learningDetection of slang words in e data using semi supervised learning
Detection of slang words in e data using semi supervised learningijaia
 
IRJET - Response Analysis of Educational Videos
IRJET - Response Analysis of Educational VideosIRJET - Response Analysis of Educational Videos
IRJET - Response Analysis of Educational VideosIRJET Journal
 
NLP BASED INTERVIEW ASSESSMENT SYSTEM
NLP BASED INTERVIEW ASSESSMENT SYSTEMNLP BASED INTERVIEW ASSESSMENT SYSTEM
NLP BASED INTERVIEW ASSESSMENT SYSTEMvivatechijri
 
SEMI-SUPERVISED BOOTSTRAPPING APPROACH FOR NAMED ENTITY RECOGNITION
SEMI-SUPERVISED BOOTSTRAPPING APPROACH FOR NAMED ENTITY RECOGNITIONSEMI-SUPERVISED BOOTSTRAPPING APPROACH FOR NAMED ENTITY RECOGNITION
SEMI-SUPERVISED BOOTSTRAPPING APPROACH FOR NAMED ENTITY RECOGNITIONkevig
 
Pattern Recognition of Japanese Alphabet Katakana Using Airy Zeta Function
Pattern Recognition of Japanese Alphabet Katakana Using Airy Zeta FunctionPattern Recognition of Japanese Alphabet Katakana Using Airy Zeta Function
Pattern Recognition of Japanese Alphabet Katakana Using Airy Zeta FunctionEditor IJCATR
 
TEXT SENTIMENTS FOR FORUMS HOTSPOT DETECTION
TEXT SENTIMENTS FOR FORUMS HOTSPOT DETECTIONTEXT SENTIMENTS FOR FORUMS HOTSPOT DETECTION
TEXT SENTIMENTS FOR FORUMS HOTSPOT DETECTIONijistjournal
 
A fuzzy logic based on sentiment
A fuzzy logic based on sentimentA fuzzy logic based on sentiment
A fuzzy logic based on sentimentIJDKP
 
Mining of product reviews at aspect level
Mining of product reviews at aspect levelMining of product reviews at aspect level
Mining of product reviews at aspect levelijfcstjournal
 
Top C Language Interview Questions and Answer
Top C Language Interview Questions and AnswerTop C Language Interview Questions and Answer
Top C Language Interview Questions and AnswerVineet Kumar Saini
 
Implementation of Fuzzy Tsukamoto Algorithm in Determining Work Feasibility
Implementation of Fuzzy Tsukamoto Algorithm in Determining Work FeasibilityImplementation of Fuzzy Tsukamoto Algorithm in Determining Work Feasibility
Implementation of Fuzzy Tsukamoto Algorithm in Determining Work FeasibilityUniversitas Pembangunan Panca Budi
 
Mikel_Berdufi_SemanticWebSearchEngine_Report
Mikel_Berdufi_SemanticWebSearchEngine_ReportMikel_Berdufi_SemanticWebSearchEngine_Report
Mikel_Berdufi_SemanticWebSearchEngine_ReportMikel Berdufi
 
EXTRACTING BUSINESS INTELLIGENCE FROM ONLINE PRODUCT REVIEWS
EXTRACTING BUSINESS INTELLIGENCE FROM ONLINE PRODUCT REVIEWSEXTRACTING BUSINESS INTELLIGENCE FROM ONLINE PRODUCT REVIEWS
EXTRACTING BUSINESS INTELLIGENCE FROM ONLINE PRODUCT REVIEWSijdms
 

What's hot (18)

TOWARDS MAKING SENSE OF ONLINE REVIEWS BASED ON STATEMENT EXTRACTION
TOWARDS MAKING SENSE OF ONLINE REVIEWS BASED ON STATEMENT EXTRACTIONTOWARDS MAKING SENSE OF ONLINE REVIEWS BASED ON STATEMENT EXTRACTION
TOWARDS MAKING SENSE OF ONLINE REVIEWS BASED ON STATEMENT EXTRACTION
 
Detection of slang words in e data using semi supervised learning
Detection of slang words in e data using semi supervised learningDetection of slang words in e data using semi supervised learning
Detection of slang words in e data using semi supervised learning
 
J1803015357
J1803015357J1803015357
J1803015357
 
G1803013542
G1803013542G1803013542
G1803013542
 
IRJET - Response Analysis of Educational Videos
IRJET - Response Analysis of Educational VideosIRJET - Response Analysis of Educational Videos
IRJET - Response Analysis of Educational Videos
 
P1803018289
P1803018289P1803018289
P1803018289
 
F0363942
F0363942F0363942
F0363942
 
NLP BASED INTERVIEW ASSESSMENT SYSTEM
NLP BASED INTERVIEW ASSESSMENT SYSTEMNLP BASED INTERVIEW ASSESSMENT SYSTEM
NLP BASED INTERVIEW ASSESSMENT SYSTEM
 
SEMI-SUPERVISED BOOTSTRAPPING APPROACH FOR NAMED ENTITY RECOGNITION
SEMI-SUPERVISED BOOTSTRAPPING APPROACH FOR NAMED ENTITY RECOGNITIONSEMI-SUPERVISED BOOTSTRAPPING APPROACH FOR NAMED ENTITY RECOGNITION
SEMI-SUPERVISED BOOTSTRAPPING APPROACH FOR NAMED ENTITY RECOGNITION
 
Pattern Recognition of Japanese Alphabet Katakana Using Airy Zeta Function
Pattern Recognition of Japanese Alphabet Katakana Using Airy Zeta FunctionPattern Recognition of Japanese Alphabet Katakana Using Airy Zeta Function
Pattern Recognition of Japanese Alphabet Katakana Using Airy Zeta Function
 
TEXT SENTIMENTS FOR FORUMS HOTSPOT DETECTION
TEXT SENTIMENTS FOR FORUMS HOTSPOT DETECTIONTEXT SENTIMENTS FOR FORUMS HOTSPOT DETECTION
TEXT SENTIMENTS FOR FORUMS HOTSPOT DETECTION
 
Correlation of feature score to to overall sentiment score for identifying th...
Correlation of feature score to to overall sentiment score for identifying th...Correlation of feature score to to overall sentiment score for identifying th...
Correlation of feature score to to overall sentiment score for identifying th...
 
A fuzzy logic based on sentiment
A fuzzy logic based on sentimentA fuzzy logic based on sentiment
A fuzzy logic based on sentiment
 
Mining of product reviews at aspect level
Mining of product reviews at aspect levelMining of product reviews at aspect level
Mining of product reviews at aspect level
 
Top C Language Interview Questions and Answer
Top C Language Interview Questions and AnswerTop C Language Interview Questions and Answer
Top C Language Interview Questions and Answer
 
Implementation of Fuzzy Tsukamoto Algorithm in Determining Work Feasibility
Implementation of Fuzzy Tsukamoto Algorithm in Determining Work FeasibilityImplementation of Fuzzy Tsukamoto Algorithm in Determining Work Feasibility
Implementation of Fuzzy Tsukamoto Algorithm in Determining Work Feasibility
 
Mikel_Berdufi_SemanticWebSearchEngine_Report
Mikel_Berdufi_SemanticWebSearchEngine_ReportMikel_Berdufi_SemanticWebSearchEngine_Report
Mikel_Berdufi_SemanticWebSearchEngine_Report
 
EXTRACTING BUSINESS INTELLIGENCE FROM ONLINE PRODUCT REVIEWS
EXTRACTING BUSINESS INTELLIGENCE FROM ONLINE PRODUCT REVIEWSEXTRACTING BUSINESS INTELLIGENCE FROM ONLINE PRODUCT REVIEWS
EXTRACTING BUSINESS INTELLIGENCE FROM ONLINE PRODUCT REVIEWS
 

Viewers also liked

Sisal and its Potential for Creating Innovative Employment Opportunities and ...
Sisal and its Potential for Creating Innovative Employment Opportunities and ...Sisal and its Potential for Creating Innovative Employment Opportunities and ...
Sisal and its Potential for Creating Innovative Employment Opportunities and ...IOSR Journals
 
Design and Analysis of Triple-Band Multi Slotted Microstrip Patch Antenna
Design and Analysis of Triple-Band Multi Slotted Microstrip Patch AntennaDesign and Analysis of Triple-Band Multi Slotted Microstrip Patch Antenna
Design and Analysis of Triple-Band Multi Slotted Microstrip Patch AntennaIOSR Journals
 
Exploring the Effect of Web Based Communications on Organizations Service Qua...
Exploring the Effect of Web Based Communications on Organizations Service Qua...Exploring the Effect of Web Based Communications on Organizations Service Qua...
Exploring the Effect of Web Based Communications on Organizations Service Qua...IOSR Journals
 
A Utility Interactive Electricity Generation Schemes with Renewable Resources
A Utility Interactive Electricity Generation Schemes with Renewable ResourcesA Utility Interactive Electricity Generation Schemes with Renewable Resources
A Utility Interactive Electricity Generation Schemes with Renewable ResourcesIOSR Journals
 
Performance Analysis of Rake Receivers in IR–UWB System
Performance Analysis of Rake Receivers in IR–UWB System Performance Analysis of Rake Receivers in IR–UWB System
Performance Analysis of Rake Receivers in IR–UWB System IOSR Journals
 
Shear Stress Prediction Using FEA-ANN Hybrid Modeling Of Eicher 11.10 Chassis...
Shear Stress Prediction Using FEA-ANN Hybrid Modeling Of Eicher 11.10 Chassis...Shear Stress Prediction Using FEA-ANN Hybrid Modeling Of Eicher 11.10 Chassis...
Shear Stress Prediction Using FEA-ANN Hybrid Modeling Of Eicher 11.10 Chassis...IOSR Journals
 
Anisotropic Bianchi Type-III Dark Energy Model with Time-Dependent Decelerati...
Anisotropic Bianchi Type-III Dark Energy Model with Time-Dependent Decelerati...Anisotropic Bianchi Type-III Dark Energy Model with Time-Dependent Decelerati...
Anisotropic Bianchi Type-III Dark Energy Model with Time-Dependent Decelerati...IOSR Journals
 
A Novel Flipflop Topology for High Speed and Area Efficient Logic Structure D...
A Novel Flipflop Topology for High Speed and Area Efficient Logic Structure D...A Novel Flipflop Topology for High Speed and Area Efficient Logic Structure D...
A Novel Flipflop Topology for High Speed and Area Efficient Logic Structure D...IOSR Journals
 
A Comparative Study of Tourism Industry in North-Eastern States of India
A Comparative Study of Tourism Industry in North-Eastern States of IndiaA Comparative Study of Tourism Industry in North-Eastern States of India
A Comparative Study of Tourism Industry in North-Eastern States of IndiaIOSR Journals
 
The Evaluation of the Seismic Vulnerability of Reinforced Concrete Building i...
The Evaluation of the Seismic Vulnerability of Reinforced Concrete Building i...The Evaluation of the Seismic Vulnerability of Reinforced Concrete Building i...
The Evaluation of the Seismic Vulnerability of Reinforced Concrete Building i...IOSR Journals
 
Restoration of Images Corrupted by High Density Salt & Pepper Noise through A...
Restoration of Images Corrupted by High Density Salt & Pepper Noise through A...Restoration of Images Corrupted by High Density Salt & Pepper Noise through A...
Restoration of Images Corrupted by High Density Salt & Pepper Noise through A...IOSR Journals
 
Fluidization Characteristics of Nano Particles with the Assist of Stirrer
Fluidization Characteristics of Nano Particles with the Assist of StirrerFluidization Characteristics of Nano Particles with the Assist of Stirrer
Fluidization Characteristics of Nano Particles with the Assist of StirrerIOSR Journals
 
Effect of glycine as an impurity on the properties of Epsomite single crystals
Effect of glycine as an impurity on the properties of Epsomite single crystalsEffect of glycine as an impurity on the properties of Epsomite single crystals
Effect of glycine as an impurity on the properties of Epsomite single crystalsIOSR Journals
 
A new universal formula for atoms, planets, and galaxies
A new universal formula for atoms, planets, and galaxiesA new universal formula for atoms, planets, and galaxies
A new universal formula for atoms, planets, and galaxiesIOSR Journals
 
All optical single module phase encoded half-adder and halfsubtractor exploit...
All optical single module phase encoded half-adder and halfsubtractor exploit...All optical single module phase encoded half-adder and halfsubtractor exploit...
All optical single module phase encoded half-adder and halfsubtractor exploit...IOSR Journals
 
Tax Morale and Its Effect on Taxpayers’ Compliance to Tax Policies of the Nig...
Tax Morale and Its Effect on Taxpayers’ Compliance to Tax Policies of the Nig...Tax Morale and Its Effect on Taxpayers’ Compliance to Tax Policies of the Nig...
Tax Morale and Its Effect on Taxpayers’ Compliance to Tax Policies of the Nig...IOSR Journals
 
Channel Equalization of WCDMA Downlink System Using Finite Length MMSE-DFE
Channel Equalization of WCDMA Downlink System Using Finite Length MMSE-DFEChannel Equalization of WCDMA Downlink System Using Finite Length MMSE-DFE
Channel Equalization of WCDMA Downlink System Using Finite Length MMSE-DFEIOSR Journals
 

Viewers also liked (20)

Sisal and its Potential for Creating Innovative Employment Opportunities and ...
Sisal and its Potential for Creating Innovative Employment Opportunities and ...Sisal and its Potential for Creating Innovative Employment Opportunities and ...
Sisal and its Potential for Creating Innovative Employment Opportunities and ...
 
Design and Analysis of Triple-Band Multi Slotted Microstrip Patch Antenna
Design and Analysis of Triple-Band Multi Slotted Microstrip Patch AntennaDesign and Analysis of Triple-Band Multi Slotted Microstrip Patch Antenna
Design and Analysis of Triple-Band Multi Slotted Microstrip Patch Antenna
 
Exploring the Effect of Web Based Communications on Organizations Service Qua...
Exploring the Effect of Web Based Communications on Organizations Service Qua...Exploring the Effect of Web Based Communications on Organizations Service Qua...
Exploring the Effect of Web Based Communications on Organizations Service Qua...
 
A Utility Interactive Electricity Generation Schemes with Renewable Resources
A Utility Interactive Electricity Generation Schemes with Renewable ResourcesA Utility Interactive Electricity Generation Schemes with Renewable Resources
A Utility Interactive Electricity Generation Schemes with Renewable Resources
 
Performance Analysis of Rake Receivers in IR–UWB System
Performance Analysis of Rake Receivers in IR–UWB System Performance Analysis of Rake Receivers in IR–UWB System
Performance Analysis of Rake Receivers in IR–UWB System
 
Shear Stress Prediction Using FEA-ANN Hybrid Modeling Of Eicher 11.10 Chassis...
Shear Stress Prediction Using FEA-ANN Hybrid Modeling Of Eicher 11.10 Chassis...Shear Stress Prediction Using FEA-ANN Hybrid Modeling Of Eicher 11.10 Chassis...
Shear Stress Prediction Using FEA-ANN Hybrid Modeling Of Eicher 11.10 Chassis...
 
Anisotropic Bianchi Type-III Dark Energy Model with Time-Dependent Decelerati...
Anisotropic Bianchi Type-III Dark Energy Model with Time-Dependent Decelerati...Anisotropic Bianchi Type-III Dark Energy Model with Time-Dependent Decelerati...
Anisotropic Bianchi Type-III Dark Energy Model with Time-Dependent Decelerati...
 
A Novel Flipflop Topology for High Speed and Area Efficient Logic Structure D...
A Novel Flipflop Topology for High Speed and Area Efficient Logic Structure D...A Novel Flipflop Topology for High Speed and Area Efficient Logic Structure D...
A Novel Flipflop Topology for High Speed and Area Efficient Logic Structure D...
 
A Comparative Study of Tourism Industry in North-Eastern States of India
A Comparative Study of Tourism Industry in North-Eastern States of IndiaA Comparative Study of Tourism Industry in North-Eastern States of India
A Comparative Study of Tourism Industry in North-Eastern States of India
 
The Evaluation of the Seismic Vulnerability of Reinforced Concrete Building i...
The Evaluation of the Seismic Vulnerability of Reinforced Concrete Building i...The Evaluation of the Seismic Vulnerability of Reinforced Concrete Building i...
The Evaluation of the Seismic Vulnerability of Reinforced Concrete Building i...
 
Restoration of Images Corrupted by High Density Salt & Pepper Noise through A...
Restoration of Images Corrupted by High Density Salt & Pepper Noise through A...Restoration of Images Corrupted by High Density Salt & Pepper Noise through A...
Restoration of Images Corrupted by High Density Salt & Pepper Noise through A...
 
F0562732
F0562732F0562732
F0562732
 
Fluidization Characteristics of Nano Particles with the Assist of Stirrer
Fluidization Characteristics of Nano Particles with the Assist of StirrerFluidization Characteristics of Nano Particles with the Assist of Stirrer
Fluidization Characteristics of Nano Particles with the Assist of Stirrer
 
A012610106
A012610106A012610106
A012610106
 
Effect of glycine as an impurity on the properties of Epsomite single crystals
Effect of glycine as an impurity on the properties of Epsomite single crystalsEffect of glycine as an impurity on the properties of Epsomite single crystals
Effect of glycine as an impurity on the properties of Epsomite single crystals
 
A new universal formula for atoms, planets, and galaxies
A new universal formula for atoms, planets, and galaxiesA new universal formula for atoms, planets, and galaxies
A new universal formula for atoms, planets, and galaxies
 
All optical single module phase encoded half-adder and halfsubtractor exploit...
All optical single module phase encoded half-adder and halfsubtractor exploit...All optical single module phase encoded half-adder and halfsubtractor exploit...
All optical single module phase encoded half-adder and halfsubtractor exploit...
 
Tax Morale and Its Effect on Taxpayers’ Compliance to Tax Policies of the Nig...
Tax Morale and Its Effect on Taxpayers’ Compliance to Tax Policies of the Nig...Tax Morale and Its Effect on Taxpayers’ Compliance to Tax Policies of the Nig...
Tax Morale and Its Effect on Taxpayers’ Compliance to Tax Policies of the Nig...
 
A0750105
A0750105A0750105
A0750105
 
Channel Equalization of WCDMA Downlink System Using Finite Length MMSE-DFE
Channel Equalization of WCDMA Downlink System Using Finite Length MMSE-DFEChannel Equalization of WCDMA Downlink System Using Finite Length MMSE-DFE
Channel Equalization of WCDMA Downlink System Using Finite Length MMSE-DFE
 

Similar to Computer aided environment for drawing (to set) fill in the blank from given paragraph.

QUESTION ANALYSIS FOR ARABIC QUESTION ANSWERING SYSTEMS
QUESTION ANALYSIS FOR ARABIC QUESTION ANSWERING SYSTEMS QUESTION ANALYSIS FOR ARABIC QUESTION ANSWERING SYSTEMS
QUESTION ANALYSIS FOR ARABIC QUESTION ANSWERING SYSTEMS ijnlc
 
What is algorithm
What is algorithmWhat is algorithm
What is algorithmmshoaib15
 
Sentiment Analysis: A comparative study of Deep Learning and Machine Learning
Sentiment Analysis: A comparative study of Deep Learning and Machine LearningSentiment Analysis: A comparative study of Deep Learning and Machine Learning
Sentiment Analysis: A comparative study of Deep Learning and Machine LearningIRJET Journal
 
Automated Essay Grading using Features Selection
Automated Essay Grading using Features SelectionAutomated Essay Grading using Features Selection
Automated Essay Grading using Features SelectionIRJET Journal
 
Programming Fundamentals lecture 3
Programming Fundamentals lecture 3Programming Fundamentals lecture 3
Programming Fundamentals lecture 3REHAN IJAZ
 
Sentiment Analysis using Twitter Data
Sentiment Analysis using Twitter DataSentiment Analysis using Twitter Data
Sentiment Analysis using Twitter DataHari Prasad
 
sentimentanaly 2.pdf
sentimentanaly 2.pdfsentimentanaly 2.pdf
sentimentanaly 2.pdfvisheshs4
 
Software develop....
Software develop.... Software develop....
Software develop.... GCWUS
 
NEr using N-Gram techniqueppt
NEr using N-Gram techniquepptNEr using N-Gram techniqueppt
NEr using N-Gram techniquepptGyandeep Kansal
 
IRJET- Public Opinion Analysis on Law Enforcement
IRJET-  	  Public Opinion Analysis on Law EnforcementIRJET-  	  Public Opinion Analysis on Law Enforcement
IRJET- Public Opinion Analysis on Law EnforcementIRJET Journal
 
Hrms industrial training report
Hrms industrial training reportHrms industrial training report
Hrms industrial training reportNitesh Dubey
 
EXPERIMENTAL EVALUATION AND RESULT DISCUSSION OF METAMORPHIC TESTING AUTOMATI...
EXPERIMENTAL EVALUATION AND RESULT DISCUSSION OF METAMORPHIC TESTING AUTOMATI...EXPERIMENTAL EVALUATION AND RESULT DISCUSSION OF METAMORPHIC TESTING AUTOMATI...
EXPERIMENTAL EVALUATION AND RESULT DISCUSSION OF METAMORPHIC TESTING AUTOMATI...IAEME Publication
 
CLUSTER PRIORITY BASED SENTENCE RANKING FOR EFFICIENT EXTRACTIVE TEXT SUMMARIES
CLUSTER PRIORITY BASED SENTENCE RANKING FOR EFFICIENT EXTRACTIVE TEXT SUMMARIESCLUSTER PRIORITY BASED SENTENCE RANKING FOR EFFICIENT EXTRACTIVE TEXT SUMMARIES
CLUSTER PRIORITY BASED SENTENCE RANKING FOR EFFICIENT EXTRACTIVE TEXT SUMMARIESecij
 

Similar to Computer aided environment for drawing (to set) fill in the blank from given paragraph. (20)

R04503105108
R04503105108R04503105108
R04503105108
 
QUESTION ANALYSIS FOR ARABIC QUESTION ANSWERING SYSTEMS
QUESTION ANALYSIS FOR ARABIC QUESTION ANSWERING SYSTEMS QUESTION ANALYSIS FOR ARABIC QUESTION ANSWERING SYSTEMS
QUESTION ANALYSIS FOR ARABIC QUESTION ANSWERING SYSTEMS
 
What is algorithm
What is algorithmWhat is algorithm
What is algorithm
 
Sentiment Analysis: A comparative study of Deep Learning and Machine Learning
Sentiment Analysis: A comparative study of Deep Learning and Machine LearningSentiment Analysis: A comparative study of Deep Learning and Machine Learning
Sentiment Analysis: A comparative study of Deep Learning and Machine Learning
 
Automated Essay Grading using Features Selection
Automated Essay Grading using Features SelectionAutomated Essay Grading using Features Selection
Automated Essay Grading using Features Selection
 
Programming Fundamentals lecture 3
Programming Fundamentals lecture 3Programming Fundamentals lecture 3
Programming Fundamentals lecture 3
 
Sentiment Analysis using Twitter Data
Sentiment Analysis using Twitter DataSentiment Analysis using Twitter Data
Sentiment Analysis using Twitter Data
 
sentimentanaly 2.pdf
sentimentanaly 2.pdfsentimentanaly 2.pdf
sentimentanaly 2.pdf
 
Software develop....
Software develop.... Software develop....
Software develop....
 
Software Development
Software DevelopmentSoftware Development
Software Development
 
NEr using N-Gram techniqueppt
NEr using N-Gram techniquepptNEr using N-Gram techniqueppt
NEr using N-Gram techniqueppt
 
Final ppt
Final pptFinal ppt
Final ppt
 
IRJET- Public Opinion Analysis on Law Enforcement
IRJET-  	  Public Opinion Analysis on Law EnforcementIRJET-  	  Public Opinion Analysis on Law Enforcement
IRJET- Public Opinion Analysis on Law Enforcement
 
A Review on Novel Scoring System for Identify Accurate Answers for Factoid Qu...
A Review on Novel Scoring System for Identify Accurate Answers for Factoid Qu...A Review on Novel Scoring System for Identify Accurate Answers for Factoid Qu...
A Review on Novel Scoring System for Identify Accurate Answers for Factoid Qu...
 
Hrms industrial training report
Hrms industrial training reportHrms industrial training report
Hrms industrial training report
 
Scenario 4
Scenario 4Scenario 4
Scenario 4
 
Novel Scoring System for Identify Accurate Answers for Factoid Questions
Novel Scoring System for Identify Accurate Answers for Factoid QuestionsNovel Scoring System for Identify Accurate Answers for Factoid Questions
Novel Scoring System for Identify Accurate Answers for Factoid Questions
 
Poster (2)
Poster (2)Poster (2)
Poster (2)
 
EXPERIMENTAL EVALUATION AND RESULT DISCUSSION OF METAMORPHIC TESTING AUTOMATI...
EXPERIMENTAL EVALUATION AND RESULT DISCUSSION OF METAMORPHIC TESTING AUTOMATI...EXPERIMENTAL EVALUATION AND RESULT DISCUSSION OF METAMORPHIC TESTING AUTOMATI...
EXPERIMENTAL EVALUATION AND RESULT DISCUSSION OF METAMORPHIC TESTING AUTOMATI...
 
CLUSTER PRIORITY BASED SENTENCE RANKING FOR EFFICIENT EXTRACTIVE TEXT SUMMARIES
CLUSTER PRIORITY BASED SENTENCE RANKING FOR EFFICIENT EXTRACTIVE TEXT SUMMARIESCLUSTER PRIORITY BASED SENTENCE RANKING FOR EFFICIENT EXTRACTIVE TEXT SUMMARIES
CLUSTER PRIORITY BASED SENTENCE RANKING FOR EFFICIENT EXTRACTIVE TEXT SUMMARIES
 

More from IOSR Journals (20)

A011140104
A011140104A011140104
A011140104
 
M0111397100
M0111397100M0111397100
M0111397100
 
L011138596
L011138596L011138596
L011138596
 
K011138084
K011138084K011138084
K011138084
 
J011137479
J011137479J011137479
J011137479
 
I011136673
I011136673I011136673
I011136673
 
G011134454
G011134454G011134454
G011134454
 
H011135565
H011135565H011135565
H011135565
 
F011134043
F011134043F011134043
F011134043
 
E011133639
E011133639E011133639
E011133639
 
D011132635
D011132635D011132635
D011132635
 
C011131925
C011131925C011131925
C011131925
 
B011130918
B011130918B011130918
B011130918
 
A011130108
A011130108A011130108
A011130108
 
I011125160
I011125160I011125160
I011125160
 
H011124050
H011124050H011124050
H011124050
 
G011123539
G011123539G011123539
G011123539
 
F011123134
F011123134F011123134
F011123134
 
E011122530
E011122530E011122530
E011122530
 
D011121524
D011121524D011121524
D011121524
 

Recently uploaded

College Call Girls Nashik Nehal 7001305949 Independent Escort Service Nashik
College Call Girls Nashik Nehal 7001305949 Independent Escort Service NashikCollege Call Girls Nashik Nehal 7001305949 Independent Escort Service Nashik
College Call Girls Nashik Nehal 7001305949 Independent Escort Service NashikCall Girls in Nagpur High Profile
 
GDSC ASEB Gen AI study jams presentation
GDSC ASEB Gen AI study jams presentationGDSC ASEB Gen AI study jams presentation
GDSC ASEB Gen AI study jams presentationGDSCAESB
 
OSVC_Meta-Data based Simulation Automation to overcome Verification Challenge...
OSVC_Meta-Data based Simulation Automation to overcome Verification Challenge...OSVC_Meta-Data based Simulation Automation to overcome Verification Challenge...
OSVC_Meta-Data based Simulation Automation to overcome Verification Challenge...Soham Mondal
 
Sheet Pile Wall Design and Construction: A Practical Guide for Civil Engineer...
Sheet Pile Wall Design and Construction: A Practical Guide for Civil Engineer...Sheet Pile Wall Design and Construction: A Practical Guide for Civil Engineer...
Sheet Pile Wall Design and Construction: A Practical Guide for Civil Engineer...Dr.Costas Sachpazis
 
Introduction and different types of Ethernet.pptx
Introduction and different types of Ethernet.pptxIntroduction and different types of Ethernet.pptx
Introduction and different types of Ethernet.pptxupamatechverse
 
the ladakh protest in leh ladakh 2024 sonam wangchuk.pptx
the ladakh protest in leh ladakh 2024 sonam wangchuk.pptxthe ladakh protest in leh ladakh 2024 sonam wangchuk.pptx
the ladakh protest in leh ladakh 2024 sonam wangchuk.pptxhumanexperienceaaa
 
Decoding Kotlin - Your guide to solving the mysterious in Kotlin.pptx
Decoding Kotlin - Your guide to solving the mysterious in Kotlin.pptxDecoding Kotlin - Your guide to solving the mysterious in Kotlin.pptx
Decoding Kotlin - Your guide to solving the mysterious in Kotlin.pptxJoão Esperancinha
 
High Profile Call Girls Nagpur Isha Call 7001035870 Meet With Nagpur Escorts
High Profile Call Girls Nagpur Isha Call 7001035870 Meet With Nagpur EscortsHigh Profile Call Girls Nagpur Isha Call 7001035870 Meet With Nagpur Escorts
High Profile Call Girls Nagpur Isha Call 7001035870 Meet With Nagpur Escortsranjana rawat
 
Extrusion Processes and Their Limitations
Extrusion Processes and Their LimitationsExtrusion Processes and Their Limitations
Extrusion Processes and Their Limitations120cr0395
 
Call for Papers - African Journal of Biological Sciences, E-ISSN: 2663-2187, ...
Call for Papers - African Journal of Biological Sciences, E-ISSN: 2663-2187, ...Call for Papers - African Journal of Biological Sciences, E-ISSN: 2663-2187, ...
Call for Papers - African Journal of Biological Sciences, E-ISSN: 2663-2187, ...Christo Ananth
 
chaitra-1.pptx fake news detection using machine learning
chaitra-1.pptx  fake news detection using machine learningchaitra-1.pptx  fake news detection using machine learning
chaitra-1.pptx fake news detection using machine learningmisbanausheenparvam
 
Introduction to Multiple Access Protocol.pptx
Introduction to Multiple Access Protocol.pptxIntroduction to Multiple Access Protocol.pptx
Introduction to Multiple Access Protocol.pptxupamatechverse
 
SPICE PARK APR2024 ( 6,793 SPICE Models )
SPICE PARK APR2024 ( 6,793 SPICE Models )SPICE PARK APR2024 ( 6,793 SPICE Models )
SPICE PARK APR2024 ( 6,793 SPICE Models )Tsuyoshi Horigome
 
(SHREYA) Chakan Call Girls Just Call 7001035870 [ Cash on Delivery ] Pune Esc...
(SHREYA) Chakan Call Girls Just Call 7001035870 [ Cash on Delivery ] Pune Esc...(SHREYA) Chakan Call Girls Just Call 7001035870 [ Cash on Delivery ] Pune Esc...
(SHREYA) Chakan Call Girls Just Call 7001035870 [ Cash on Delivery ] Pune Esc...ranjana rawat
 
Call Girls Delhi {Jodhpur} 9711199012 high profile service
Call Girls Delhi {Jodhpur} 9711199012 high profile serviceCall Girls Delhi {Jodhpur} 9711199012 high profile service
Call Girls Delhi {Jodhpur} 9711199012 high profile servicerehmti665
 
MANUFACTURING PROCESS-II UNIT-2 LATHE MACHINE
MANUFACTURING PROCESS-II UNIT-2 LATHE MACHINEMANUFACTURING PROCESS-II UNIT-2 LATHE MACHINE
MANUFACTURING PROCESS-II UNIT-2 LATHE MACHINESIVASHANKAR N
 
Coefficient of Thermal Expansion and their Importance.pptx
Coefficient of Thermal Expansion and their Importance.pptxCoefficient of Thermal Expansion and their Importance.pptx
Coefficient of Thermal Expansion and their Importance.pptxAsutosh Ranjan
 

Recently uploaded (20)

DJARUM4D - SLOT GACOR ONLINE | SLOT DEMO ONLINE
DJARUM4D - SLOT GACOR ONLINE | SLOT DEMO ONLINEDJARUM4D - SLOT GACOR ONLINE | SLOT DEMO ONLINE
DJARUM4D - SLOT GACOR ONLINE | SLOT DEMO ONLINE
 
College Call Girls Nashik Nehal 7001305949 Independent Escort Service Nashik
College Call Girls Nashik Nehal 7001305949 Independent Escort Service NashikCollege Call Girls Nashik Nehal 7001305949 Independent Escort Service Nashik
College Call Girls Nashik Nehal 7001305949 Independent Escort Service Nashik
 
GDSC ASEB Gen AI study jams presentation
GDSC ASEB Gen AI study jams presentationGDSC ASEB Gen AI study jams presentation
GDSC ASEB Gen AI study jams presentation
 
OSVC_Meta-Data based Simulation Automation to overcome Verification Challenge...
OSVC_Meta-Data based Simulation Automation to overcome Verification Challenge...OSVC_Meta-Data based Simulation Automation to overcome Verification Challenge...
OSVC_Meta-Data based Simulation Automation to overcome Verification Challenge...
 
Sheet Pile Wall Design and Construction: A Practical Guide for Civil Engineer...
Sheet Pile Wall Design and Construction: A Practical Guide for Civil Engineer...Sheet Pile Wall Design and Construction: A Practical Guide for Civil Engineer...
Sheet Pile Wall Design and Construction: A Practical Guide for Civil Engineer...
 
Introduction and different types of Ethernet.pptx
Introduction and different types of Ethernet.pptxIntroduction and different types of Ethernet.pptx
Introduction and different types of Ethernet.pptx
 
the ladakh protest in leh ladakh 2024 sonam wangchuk.pptx
the ladakh protest in leh ladakh 2024 sonam wangchuk.pptxthe ladakh protest in leh ladakh 2024 sonam wangchuk.pptx
the ladakh protest in leh ladakh 2024 sonam wangchuk.pptx
 
Decoding Kotlin - Your guide to solving the mysterious in Kotlin.pptx
Decoding Kotlin - Your guide to solving the mysterious in Kotlin.pptxDecoding Kotlin - Your guide to solving the mysterious in Kotlin.pptx
Decoding Kotlin - Your guide to solving the mysterious in Kotlin.pptx
 
High Profile Call Girls Nagpur Isha Call 7001035870 Meet With Nagpur Escorts
High Profile Call Girls Nagpur Isha Call 7001035870 Meet With Nagpur EscortsHigh Profile Call Girls Nagpur Isha Call 7001035870 Meet With Nagpur Escorts
High Profile Call Girls Nagpur Isha Call 7001035870 Meet With Nagpur Escorts
 
Extrusion Processes and Their Limitations
Extrusion Processes and Their LimitationsExtrusion Processes and Their Limitations
Extrusion Processes and Their Limitations
 
Exploring_Network_Security_with_JA3_by_Rakesh Seal.pptx
Exploring_Network_Security_with_JA3_by_Rakesh Seal.pptxExploring_Network_Security_with_JA3_by_Rakesh Seal.pptx
Exploring_Network_Security_with_JA3_by_Rakesh Seal.pptx
 
★ CALL US 9953330565 ( HOT Young Call Girls In Badarpur delhi NCR
★ CALL US 9953330565 ( HOT Young Call Girls In Badarpur delhi NCR★ CALL US 9953330565 ( HOT Young Call Girls In Badarpur delhi NCR
★ CALL US 9953330565 ( HOT Young Call Girls In Badarpur delhi NCR
 
Call for Papers - African Journal of Biological Sciences, E-ISSN: 2663-2187, ...
Call for Papers - African Journal of Biological Sciences, E-ISSN: 2663-2187, ...Call for Papers - African Journal of Biological Sciences, E-ISSN: 2663-2187, ...
Call for Papers - African Journal of Biological Sciences, E-ISSN: 2663-2187, ...
 
chaitra-1.pptx fake news detection using machine learning
chaitra-1.pptx  fake news detection using machine learningchaitra-1.pptx  fake news detection using machine learning
chaitra-1.pptx fake news detection using machine learning
 
Introduction to Multiple Access Protocol.pptx
Introduction to Multiple Access Protocol.pptxIntroduction to Multiple Access Protocol.pptx
Introduction to Multiple Access Protocol.pptx
 
SPICE PARK APR2024 ( 6,793 SPICE Models )
SPICE PARK APR2024 ( 6,793 SPICE Models )SPICE PARK APR2024 ( 6,793 SPICE Models )
SPICE PARK APR2024 ( 6,793 SPICE Models )
 
(SHREYA) Chakan Call Girls Just Call 7001035870 [ Cash on Delivery ] Pune Esc...
(SHREYA) Chakan Call Girls Just Call 7001035870 [ Cash on Delivery ] Pune Esc...(SHREYA) Chakan Call Girls Just Call 7001035870 [ Cash on Delivery ] Pune Esc...
(SHREYA) Chakan Call Girls Just Call 7001035870 [ Cash on Delivery ] Pune Esc...
 
Call Girls Delhi {Jodhpur} 9711199012 high profile service
Call Girls Delhi {Jodhpur} 9711199012 high profile serviceCall Girls Delhi {Jodhpur} 9711199012 high profile service
Call Girls Delhi {Jodhpur} 9711199012 high profile service
 
MANUFACTURING PROCESS-II UNIT-2 LATHE MACHINE
MANUFACTURING PROCESS-II UNIT-2 LATHE MACHINEMANUFACTURING PROCESS-II UNIT-2 LATHE MACHINE
MANUFACTURING PROCESS-II UNIT-2 LATHE MACHINE
 
Coefficient of Thermal Expansion and their Importance.pptx
Coefficient of Thermal Expansion and their Importance.pptxCoefficient of Thermal Expansion and their Importance.pptx
Coefficient of Thermal Expansion and their Importance.pptx
 

Computer aided environment for drawing (to set) fill in the blank from given paragraph.

  • 1. IOSR Journal of Computer Engineering (IOSR-JCE) e-ISSN: 2278-0661, p- ISSN: 2278-8727Volume 15, Issue 6 (Nov. - Dec. 2013), PP 54-58 www.iosrjournals.org www.iosrjournals.org 54 | Page Computer aided environment for drawing (to set) fill in the blank from given paragraph. Sheetal Rakangor and Dr. Yogesh Ghodasara Lecturer, Depatment of Computer Science, Saurashtra University, Rajkot 360001 Associate Professor, College of Agricultural Information Technology, Anand 388110 Abstract: In this paper, we present Development of computer aided environment for drawing (to set) fill in the blanks that can generate for given paragraph. The System finds fill in the blanks, blanking key generates from the selected statement. Syntactic and lexical features are used in this process. NLP parser is used, part of speech taggers are applied on each of these sentences to encode necessary information. We present our work in designing and implementing the system which generate the blanks. The System is developed in Java using JDBC which is open source. Keyword: Natural Language Processing NLP, Key selection, POS tagging, Sentence selection I. Introduction Now a day, increasing in population affects the number of students in every education stream. If we compare the scenario before 10 to 15 years with the current scenario, the number of students in each stream increases rapidly. In any educational system, student has to appear for the examination to determine their performance and depending upon their performance they will be promoted for the next year. To evaluate the performance of the student different evaluation methods i.e. examination system are implemented. Fill in the blank questions where one or more words are removed from a sentence/paragraph, are easy to evaluate. Preparing this question manually required lots of time and efforts so with the help of this system teacher can automatically generate the fill in the blanks question from given paragraph. Some of the earlier attempts in generating fill in the blanks are given below: One of the first automatic question generation systems proposed for supporting learning activities was AUTOQUEST [2]. Used pattern matching approach to generate question from one of the sentences and grade the student answer. The purpose of these questions is to help novices to learn English. Automatic gaps fill question generation system by Manish Agrawal and prashant mannem [4] generates fill in the blanks with options generated from text book without external resources except the document from text book. Sentence Selection, Key Selection and distractor are selected from chapter only. POS tagger and typed dependency is used for generating question. TEDDCLOG [5] System take input as key and finds distractor from distributional thesaurus and find the sentence having inputted key and does not have distractor. Then system presents the sentence with distractor for approval, modification and rejection. MCTI [6] system is semi automatic system consist following task Key Term Extraction, Source Clause Selection, Transformation to stem and Distractor Selection. In this system first key Term Identification is done than on that basis sentence And then Transformation of stem means convert the sentence into wh-phrase. And last distractors are created. Computer aided multiple choice test items [8] is created post-editing interface where term extraction, sentence transformation, and for distractor selection Wordnet and corpus external resources were used. In this system post editing is used and user friendly interface is used for modification of question. And provide list of multiple distractor, user can select distractor’s from that list for MCQ generated by system. Most of the system approaches use is to select key first, and then sentence is selected. We have given priority to sentence and then from the important sentence key is extracted.
  • 2. Computer aided environment for drawing (to set) fill in the blank from given paragraph. www.iosrjournals.org 55 | Page II. Model And Methodology The following goals were set for the proposed computerized system for drawing question paper, from given paragraph: Fill in the blanks questions are generated from it in two stages: sentence selection and key selection Sentence selection involves identifying sentences in the paragraph which can be used to generate question. These sentences are then processed in the key selection stage to identify the key on which to ask the question. Architecture of System In the Architecture of the system, User will input the paragraph, through interactive user friendly editor. Figure 1 : Architecture of System In this approach user will enter the paragraph from the system. And extract the sentences and select the important sentence from the given paragraph. Using certain features which are explained below, then key list is generated from the important sentence selection. From the key list, potential key is extracted from system and blank is generated from the given paragraph. Sentence Selection In Sentence Selection module goes through all sentences from given paragraph and used NLP Stanford parser which parser the sentences and divided into small fragments called token. And from that token POS tagger is used, which provides a representation of grammatical relations between words in a sentence. And extract a set of features like, Count number of Sentences: Paragraph entered count number of sentences from that paragraph. Count number of words: Count number of words in the sentence. Short sentence generate unanswerable question because short content and very long content might have enough content to make the question generated. Count number of nouns: Noun gives an idea about the sentences, if maximum number of noun in sentence means potential key can be generated from that sentence and that sentence having good content which can generate the key for fill in the blanks. Superlative: Superlative degree defines exaggerated mode of expression or height of quality. Superlative are typically formed with suffix –est (healthiest) or the word most, good, best are used. Sentence which contains superlative degree can generate good fill in the blanks. On the basis of these features important sentence will be extracted from the given paragraph. Algorithm for sentence selection 1. Enter the paragraph P 2. Read the statements from the paragraph S. 3. Calculate number of sentences CtS. 4. Calculate number of words from each sentence CtW 5. For each CtNoun and CtSuper from S do Select the sentence which contains superlative degree and then calculate maximum number of nouns which contain superlative degree. 6. IF CtSuper and CtNoun from S then
  • 3. Computer aided environment for drawing (to set) fill in the blank from given paragraph. www.iosrjournals.org 56 | Page SetenceSelected SS ElseIF Max (CtNoun) from S then SetenceSelected SS If there is no superlative degree then select that sentence which having maximum number of noun Else Without Noun and Superlative degree, blanks will not generated EndIF EndFor 7. Display SentenceSelected SS Key Selection Key selection is most important stage, to identify the key from important sentence to ask the question on. Previous work in this area, [5] takes key as input and [6] select key on basis of regular expression on noun. Or first search the key and then basic sentence is selected. Our key selection approach is divided into two stages. Generate Potential keys from the statement and select Best Key form that key list [4]. First Stage: Potential keys are generated from selected sentences, POS tagger is used to identify the words and there type. Suppose we need to generate key for noun then select total numbers of noun available in sentence and list them, and if any noun word is repeated in that list, it would be removed from list [4]. Second Stage: Best keys are generated from the key-list, select the word from key-list and search that word in paragraph. Count how many times that word has been used in paragraph. Select best key which has found in selected sentence and noun repeated in paragraph maximum number of times. In our system we have used Stanford parser through which POS tagger will use for inputted paragraph. Algorithm for key selection 1. For Each Word from SS do From selected sentence extract the potential key now suppose we need to generate key of noun then extract the nouns from the sentence. And add them into the keylist. KeyList =Select Noun from SS BestKey = No of Occurrence of that key in SS and Height of that key in the syntactic tree Structure. End For 2. Remove that BestKey from sentence SS and generate Fill in the blanks. So this way system will generate the fill in the blanks from given paragraph, below figure shows the how system works when user entered user enters the paragraph with example.
  • 4. Computer aided environment for drawing (to set) fill in the blank from given paragraph. www.iosrjournals.org 57 | Page Figure 2: Process flow diagram of System III. Evaluation And Result The System has tested with number of small and simple paragraph. The table shows some of the implementation of paragraphs, first sentence is selected on the basis of the features describes earlier and best key is selected from the selected sentence and then fill in the blanks is generated from the given paragraph. And Figure 3 shows screen short of the system how system generates the fill in the blanks. The system is developed in Java using JDBC for generating fill in the blanks. No. Entered Paragraph Selected Sentence Key selected Fill in the blank generated. 1 Rajkot is clean City . In Rajkot there are many beautiful places where we can go for outing . Rajkot City is part of Gujarat . Rajkot City is part of Gujarat Rajkot ____________City is part of Gujarat 2 Shrusti is the most sincere student in the class . while soniya is the most mischievous student. In fact Shrusti and soniya are most regular student. While kruti is the most beautiful student. Overall my class is the best of all. In fact Shrusti and soniya are most regular student. Shrusti In fact _________ and soniya are most regular student 3 Sachin Ramesh Tendulkar born 24 April 1973. Sachin is a former Indian cricketer widely acknowledged as the greatest batsman of the modern. Sachin took up cricket at the age of eleven, made his Test debut against Pakistan at the age of sixteen, and went on to represent Mumbai domestically and India internationally for close to twenty-four years. Sachin took up cricket at the age of eleven, made his Test debut against Pakistan at the age of sixteen, and went on to represent Mumbai domestically and India internationally for close to twenty-four years. Sachin ____________took up cricket at the age of eleven , made his Test debut against Pakistan at the age of sixteen , and went on to represent Mumbai domestically and India internationally for close to twenty-four years 4 Butterflies are one of the most beautiful and interesting creatures on earth. Butterflies attractive, brilliant colors catch interest immediately. They have wings of varying shapes and sizes, and some even appear to Butterflies are one of the most beautiful and interesting creatures on earth Butterflies ___________ are one of the most beautiful and interesting creatures on earth
  • 5. Computer aided environment for drawing (to set) fill in the blank from given paragraph. www.iosrjournals.org 58 | Page have eyes on them. 5 The peacock is the national bird of India. They have colourful feathers, two legs and a small beak. They are famous for their dance. When a peacock dances it spreads its feathers like a fan. It has a long shiny dark blue neck. Peacocks are mostly found in the fields they are very beautiful birds. The females are known as 'Peahen. Their feathers are used for making jackets, purses etc. We can see them in a zoo. The peacock is the national bird of India Peacock The ____________is the national bird of India Table :1 Example of Paragraphs tested in system. Figure 3: A Sample Screen short of the system. IV. Conclusion And Future Work The system select important sentence from the given paragraph, and generate fill in the blanks question on them. Syntactic features helped in quality of fill in the blanks generated. We look forward to generate large number of blanks from the given paragraph as well as generate MCQS and True or False from given paragraph. Evaluation of Single blank generated is done in Java with the help of Stanford parser. References [1] Pollock, M.J., Whittington, C.D., Doughty, G.F.: Evaluating the Costs and Benefits of Changing to CAA. Proceedings of the Fourth International Computer Assisted Conference CAA, http://www.caaconference.com/. (2000). [2] Wolfe, J.H.: Automatic question generation from text - an aid to independent study. SIGCUE Outlook 10(SI) (1976) [3] Kunichika, H., Katayama, T., Hirashima, T., Takeuchi, A.: Automated question generation methods for intelligent english learning systems and its evaluation, Proc. of ICCE01 (2001). [4] Manish Agarwal and Prashanth Mannem : Automatic Gap-fill Question generation from text books [5] Simon Smith, P.V.S Avinesh and Adam Kilgarriff. 2010.Gap-fill Tests for Language Learners: Corpus-Driven Item Generation . [6] Nikiforos Karamanis, Le An Ha and Ruslan Mitkov: Generating Multiple-Choice Test Items from Medical Text:A Pilot Study [7] Naveed Afzal and Viktor Pekar: Unsupervised Relation Extraction for Automatic Generation of Multiple-Choice Questions. [8] RUSLAN MITKOV, LE AN HA and NIKIFOROS KARAMANIS: A computer-aided environment for generating multiple-choice test items(2005) [9] Le An Ha :Multiple-choice test item generation: A demo Vasile Rus, Brendan Wyse, Paul Piwek, Mihai Lintean, Svetlana Stoyanchev and Cristian Moldovan:The First Question Generation Shared Task Evaluation Challenge (2010) [10] Husam Ali Yllias Chali Sadid A. Hasan: Automatic Question Generation from Sentences(2010) [11] Takuya Goto, Tomoko Kojiri, Toyohide Watanabe, Tomoharu Iwata, Takeshi Yamada : Automatic Generation System of Multiple-Choice Cloze Questions and its Evaluation [12] John Lee, Stephanie Seneff: Automatic Generation of Cloze Items for Prepositions (2007) [13] Amisha Shingala, Rinku Chavda and Paresh Virparia : NATURAL LANGUAGE INTERFACE FOR STUDENT INFORMATION SYSTEM (NLSIS). Prajna(journal) Vol.19 41 - 44 (2011)