An Efficient Cross-Lingual BERT Model for Text Classification and Named Entity Extraction in Multilingual Dataset Article Swipe
Asoke Nath
,
Debapriya Kandar
,
Rahul Gupta
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.32628/cseit217353
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.32628/cseit217353
In recent times, with the rise of the internet, everyone is being bombarded with tons of information and data from various sources like websites, blogs and articles, social media posts and comments, e-news portals etc. Now all these data are mostly unstructured. In this paper, the authors have tried to explore the efficiency of the cross-lingual BERT model i.e. M-BERT for text classification and named entity extraction on multilingual data. The authors have used datasets of three different languages namely: French, German and Portuguese to evaluate the model performance.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.32628/cseit217353
- OA Status
- diamond
- Cited By
- 3
- References
- 8
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3171527489
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W3171527489Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.32628/cseit217353Digital Object Identifier
- Title
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An Efficient Cross-Lingual BERT Model for Text Classification and Named Entity Extraction in Multilingual DatasetWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
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2021-05-12Full publication date if available
- Authors
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Asoke Nath, Debapriya Kandar, Rahul GuptaList of authors in order
- Landing page
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https://doi.org/10.32628/cseit217353Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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diamondOpen access status per OpenAlex
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https://doi.org/10.32628/cseit217353Direct OA link when available
- Concepts
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Computer science, German, Portuguese, Named-entity recognition, The Internet, Information retrieval, Natural language processing, Information extraction, World Wide Web, Social media, Artificial intelligence, Linguistics, Task (project management), Philosophy, Management, EconomicsTop concepts (fields/topics) attached by OpenAlex
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3Total citation count in OpenAlex
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2024: 1, 2023: 1, 2022: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.comments, | 31 |
| abstract_inverted_index.different | 77 |
| abstract_inverted_index.internet, | 8 |
| abstract_inverted_index.languages | 78 |
| abstract_inverted_index.websites, | 23 |
| abstract_inverted_index.Portuguese | 83 |
| abstract_inverted_index.efficiency | 52 |
| abstract_inverted_index.extraction | 66 |
| abstract_inverted_index.information | 16 |
| abstract_inverted_index.multilingual | 68 |
| abstract_inverted_index.performance. | 88 |
| abstract_inverted_index.cross-lingual | 55 |
| abstract_inverted_index.unstructured. | 41 |
| abstract_inverted_index.classification | 62 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.7900000214576721 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile.value | 0.68706103 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |