Using Syntax and Shallow Semantic Analysis for Vietnamese Question Generation Article Swipe
Phuoc Tran
,
Duy Nguyen
,
Tram T. Tran
,
Bay Vo
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3837/tiis.2023.10.007
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3837/tiis.2023.10.007
This paper presents a method of using syntax and shallow semantic analysis for Vietnamese question generation (QG).Specifically, our proposed technique concentrates on investigating both the syntactic and shallow semantic structure of each sentence.The main goal of our method is to generate questions from a single sentence.These generated questions are known as factoid questions which require short, fact-based answers.In general, syntax-based analysis is one of the most popular approaches within the QG field, but it requires linguistic expert knowledge as well as a deep understanding of syntax rules in the Vietnamese language.It is thus considered a high-cost and inefficient solution due to the requirement of significant human effort to achieve qualified syntax rules.To deal with this problem, we collected the syntax rules in Vietnamese from a Vietnamese language textbook.Moreover, we also used different natural language processing (NLP) techniques to analyze Vietnamese shallow syntax and semantics for the QG task.These techniques include: sentence segmentation, word segmentation, part of speech, chunking, dependency parsing, and named entity recognition.We used human evaluation to assess the credibility of our model, which means we manually generated questions from the corpus, and then compared them with the generated questions.The empirical evidence demonstrates that our proposed technique has significant performance, in which the generated questions are very similar to those which are created by humans.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3837/tiis.2023.10.007
- https://itiis.org/digital-library/manuscript/file/56206/TIIS Vol 17, No 10-7.pdf
- OA Status
- diamond
- References
- 25
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4388441235
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4388441235Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3837/tiis.2023.10.007Digital Object Identifier
- Title
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Using Syntax and Shallow Semantic Analysis for Vietnamese Question GenerationWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2023Year of publication
- Publication date
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2023-10-31Full publication date if available
- Authors
-
Phuoc Tran, Duy Nguyen, Tram T. Tran, Bay VoList of authors in order
- Landing page
-
https://doi.org/10.3837/tiis.2023.10.007Publisher landing page
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https://itiis.org/digital-library/manuscript/file/56206/TIIS Vol 17, No 10-7.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
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https://itiis.org/digital-library/manuscript/file/56206/TIIS Vol 17, No 10-7.pdfDirect OA link when available
- Concepts
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Computer science, Vietnamese, Syntax, Natural language processing, Artificial intelligence, Linguistics, PhilosophyTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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25Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.collected | 117 |
| abstract_inverted_index.different | 131 |
| abstract_inverted_index.empirical | 191 |
| abstract_inverted_index.generated | 46, 178, 189, 204 |
| abstract_inverted_index.high-cost | 95 |
| abstract_inverted_index.knowledge | 77 |
| abstract_inverted_index.qualified | 109 |
| abstract_inverted_index.questions | 41, 47, 52, 179, 205 |
| abstract_inverted_index.semantics | 143 |
| abstract_inverted_index.structure | 29 |
| abstract_inverted_index.syntactic | 25 |
| abstract_inverted_index.technique | 19, 197 |
| abstract_inverted_index.Vietnamese | 13, 89, 122, 125, 139 |
| abstract_inverted_index.answers.In | 57 |
| abstract_inverted_index.approaches | 67 |
| abstract_inverted_index.considered | 93 |
| abstract_inverted_index.dependency | 158 |
| abstract_inverted_index.evaluation | 166 |
| abstract_inverted_index.fact-based | 56 |
| abstract_inverted_index.generation | 15 |
| abstract_inverted_index.linguistic | 75 |
| abstract_inverted_index.processing | 134 |
| abstract_inverted_index.task.These | 147 |
| abstract_inverted_index.techniques | 136, 148 |
| abstract_inverted_index.credibility | 170 |
| abstract_inverted_index.inefficient | 97 |
| abstract_inverted_index.language.It | 90 |
| abstract_inverted_index.requirement | 102 |
| abstract_inverted_index.significant | 104, 199 |
| abstract_inverted_index.concentrates | 20 |
| abstract_inverted_index.demonstrates | 193 |
| abstract_inverted_index.performance, | 200 |
| abstract_inverted_index.sentence.The | 32 |
| abstract_inverted_index.syntax-based | 59 |
| abstract_inverted_index.investigating | 22 |
| abstract_inverted_index.questions.The | 190 |
| abstract_inverted_index.segmentation, | 151, 153 |
| abstract_inverted_index.understanding | 83 |
| abstract_inverted_index.recognition.We | 163 |
| abstract_inverted_index.sentence.These | 45 |
| abstract_inverted_index.(QG).Specifically, | 16 |
| abstract_inverted_index.textbook.Moreover, | 127 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.17105707 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |