Automatic Question Generation and Evaluation Article Swipe
Parth Panchal
,
Janak Thakkar
,
Veerapathiramoorthy Pillai
,
Shweta Patil
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.51201/jusst/21/05203
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.51201/jusst/21/05203
Generation of questions from an extract is a very tedious task for humans and an even tougher one for machines. In Automatic Question Generation (AQG), it is extremely important to examine the ways in which this can be achieved with sufficient levels of accuracy and efficiency. The way in which this can be taken ahead is by using Natural Language Processing (NLP) to process the input and to work with it for AQG. Using NLP with question generation algorithms the system can generate the questions for a better understanding of the text document. The input is pre-processed before actually moving in for the question generation process. The questions formed are first checked for proper context satisfaction with the context of the input to avoid invalid or unanswerable question generation. It is then preprocessed using various NLP-based mechanisms like tokenization, named entity recognition(NER) tagging, parts of speech(POS) tagging, etc. The question generation system consists of a machine learning classification-based Fill in the blank(FIB) generator that also generates multiple choices and a rule-based approach to generate Wh-type questions. It also consists of a question evaluator where the user can evaluate the generated question. The results of these evaluations can help in improving our system further. Also, evaluation of Wh questions has been done using the BLEU score to determine whether the automatically generated questions resemble closely the human-generated ones. This system can be used in various places to help ease the question generation and also at self-evaluator systems where the students can assess themselves so as to determine their conceptual understanding. Apart from educational use, it would also be helpful in building chatbot-based applications. This work can help improve the overall understanding of the level to which the concept given is understood by the candidate and the ways in which it can be understood more properly. We have taken a simple yet effective approach to generate the questions. Our evaluation results show that our model works well on simpler sentences.
Related Topics
Concepts
Computer science
Artificial intelligence
Natural language processing
Lexical analysis
Context (archaeology)
Natural language generation
Process (computing)
Generator (circuit theory)
Task (project management)
Text generation
Natural language
Programming language
Power (physics)
Biology
Quantum mechanics
Physics
Economics
Paleontology
Management
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.51201/jusst/21/05203
- https://jusst.org/wp-content/uploads/2021/05/Automatic-Question-Generation-and-Evaluation-1.pdf
- OA Status
- diamond
- Cited By
- 2
- References
- 18
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3166763245
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3166763245Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.51201/jusst/21/05203Digital Object Identifier
- Title
-
Automatic Question Generation and EvaluationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-05-28Full publication date if available
- Authors
-
Parth Panchal, Janak Thakkar, Veerapathiramoorthy Pillai, Shweta PatilList of authors in order
- Landing page
-
https://doi.org/10.51201/jusst/21/05203Publisher landing page
- PDF URL
-
https://jusst.org/wp-content/uploads/2021/05/Automatic-Question-Generation-and-Evaluation-1.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://jusst.org/wp-content/uploads/2021/05/Automatic-Question-Generation-and-Evaluation-1.pdfDirect OA link when available
- Concepts
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Computer science, Artificial intelligence, Natural language processing, Lexical analysis, Context (archaeology), Natural language generation, Process (computing), Generator (circuit theory), Task (project management), Text generation, Natural language, Programming language, Power (physics), Biology, Quantum mechanics, Physics, Economics, Paleontology, ManagementTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2023: 1Per-year citation counts (last 5 years)
- References (count)
-
18Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.more | 302 |
| abstract_inverted_index.show | 319 |
| abstract_inverted_index.task | 10 |
| abstract_inverted_index.text | 91 |
| abstract_inverted_index.that | 163, 320 |
| abstract_inverted_index.then | 131 |
| abstract_inverted_index.this | 35, 50 |
| abstract_inverted_index.use, | 262 |
| abstract_inverted_index.used | 231 |
| abstract_inverted_index.user | 185 |
| abstract_inverted_index.very | 8 |
| abstract_inverted_index.ways | 32, 295 |
| abstract_inverted_index.well | 324 |
| abstract_inverted_index.with | 39, 69, 75, 116 |
| abstract_inverted_index.work | 68, 273 |
| abstract_inverted_index.(NLP) | 61 |
| abstract_inverted_index.Also, | 203 |
| abstract_inverted_index.Apart | 259 |
| abstract_inverted_index.Using | 73 |
| abstract_inverted_index.ahead | 54 |
| abstract_inverted_index.avoid | 123 |
| abstract_inverted_index.first | 110 |
| abstract_inverted_index.given | 287 |
| abstract_inverted_index.input | 65, 94, 121 |
| abstract_inverted_index.level | 282 |
| abstract_inverted_index.model | 322 |
| abstract_inverted_index.named | 139 |
| abstract_inverted_index.ones. | 226 |
| abstract_inverted_index.parts | 143 |
| abstract_inverted_index.score | 214 |
| abstract_inverted_index.taken | 53, 306 |
| abstract_inverted_index.their | 256 |
| abstract_inverted_index.these | 194 |
| abstract_inverted_index.using | 57, 133, 211 |
| abstract_inverted_index.where | 183, 246 |
| abstract_inverted_index.which | 34, 49, 284, 297 |
| abstract_inverted_index.works | 323 |
| abstract_inverted_index.would | 264 |
| abstract_inverted_index.(AQG), | 24 |
| abstract_inverted_index.assess | 250 |
| abstract_inverted_index.before | 97 |
| abstract_inverted_index.better | 87 |
| abstract_inverted_index.entity | 140 |
| abstract_inverted_index.formed | 108 |
| abstract_inverted_index.humans | 12 |
| abstract_inverted_index.levels | 41 |
| abstract_inverted_index.moving | 99 |
| abstract_inverted_index.places | 234 |
| abstract_inverted_index.proper | 113 |
| abstract_inverted_index.simple | 308 |
| abstract_inverted_index.system | 80, 151, 201, 228 |
| abstract_inverted_index.Natural | 58 |
| abstract_inverted_index.Wh-type | 174 |
| abstract_inverted_index.checked | 111 |
| abstract_inverted_index.choices | 167 |
| abstract_inverted_index.closely | 223 |
| abstract_inverted_index.concept | 286 |
| abstract_inverted_index.context | 114, 118 |
| abstract_inverted_index.examine | 30 |
| abstract_inverted_index.extract | 5 |
| abstract_inverted_index.helpful | 267 |
| abstract_inverted_index.improve | 276 |
| abstract_inverted_index.invalid | 124 |
| abstract_inverted_index.machine | 155 |
| abstract_inverted_index.overall | 278 |
| abstract_inverted_index.process | 63 |
| abstract_inverted_index.results | 192, 318 |
| abstract_inverted_index.simpler | 326 |
| abstract_inverted_index.systems | 245 |
| abstract_inverted_index.tedious | 9 |
| abstract_inverted_index.tougher | 16 |
| abstract_inverted_index.various | 134, 233 |
| abstract_inverted_index.whether | 217 |
| abstract_inverted_index.Language | 59 |
| abstract_inverted_index.Question | 22 |
| abstract_inverted_index.accuracy | 43 |
| abstract_inverted_index.achieved | 38 |
| abstract_inverted_index.actually | 98 |
| abstract_inverted_index.approach | 171, 311 |
| abstract_inverted_index.building | 269 |
| abstract_inverted_index.consists | 152, 178 |
| abstract_inverted_index.evaluate | 187 |
| abstract_inverted_index.further. | 202 |
| abstract_inverted_index.generate | 82, 173, 313 |
| abstract_inverted_index.learning | 156 |
| abstract_inverted_index.multiple | 166 |
| abstract_inverted_index.process. | 105 |
| abstract_inverted_index.question | 76, 103, 127, 149, 181, 239 |
| abstract_inverted_index.resemble | 222 |
| abstract_inverted_index.students | 248 |
| abstract_inverted_index.tagging, | 142, 146 |
| abstract_inverted_index.Automatic | 21 |
| abstract_inverted_index.NLP-based | 135 |
| abstract_inverted_index.candidate | 292 |
| abstract_inverted_index.determine | 216, 255 |
| abstract_inverted_index.document. | 92 |
| abstract_inverted_index.effective | 310 |
| abstract_inverted_index.evaluator | 182 |
| abstract_inverted_index.extremely | 27 |
| abstract_inverted_index.generated | 189, 220 |
| abstract_inverted_index.generates | 165 |
| abstract_inverted_index.generator | 162 |
| abstract_inverted_index.important | 28 |
| abstract_inverted_index.improving | 199 |
| abstract_inverted_index.machines. | 19 |
| abstract_inverted_index.properly. | 303 |
| abstract_inverted_index.question. | 190 |
| abstract_inverted_index.questions | 2, 84, 107, 207, 221 |
| abstract_inverted_index.Generation | 0, 23 |
| abstract_inverted_index.Processing | 60 |
| abstract_inverted_index.algorithms | 78 |
| abstract_inverted_index.blank(FIB) | 161 |
| abstract_inverted_index.conceptual | 257 |
| abstract_inverted_index.evaluation | 204, 317 |
| abstract_inverted_index.generation | 77, 104, 150, 240 |
| abstract_inverted_index.mechanisms | 136 |
| abstract_inverted_index.questions. | 175, 315 |
| abstract_inverted_index.rule-based | 170 |
| abstract_inverted_index.sentences. | 327 |
| abstract_inverted_index.sufficient | 40 |
| abstract_inverted_index.themselves | 251 |
| abstract_inverted_index.understood | 289, 301 |
| abstract_inverted_index.educational | 261 |
| abstract_inverted_index.efficiency. | 45 |
| abstract_inverted_index.evaluations | 195 |
| abstract_inverted_index.generation. | 128 |
| abstract_inverted_index.speech(POS) | 145 |
| abstract_inverted_index.preprocessed | 132 |
| abstract_inverted_index.satisfaction | 115 |
| abstract_inverted_index.unanswerable | 126 |
| abstract_inverted_index.applications. | 271 |
| abstract_inverted_index.automatically | 219 |
| abstract_inverted_index.chatbot-based | 270 |
| abstract_inverted_index.pre-processed | 96 |
| abstract_inverted_index.tokenization, | 138 |
| abstract_inverted_index.understanding | 88, 279 |
| abstract_inverted_index.self-evaluator | 244 |
| abstract_inverted_index.understanding. | 258 |
| abstract_inverted_index.human-generated | 225 |
| abstract_inverted_index.recognition(NER) | 141 |
| abstract_inverted_index.classification-based | 157 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.8600000143051147 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile.value | 0.53198608 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |