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Advances in soft computing : 20th Mexican International Conference on Artificial Intelligence, MICAI 2021, Mexico City, Mexico, October 25-30, 2021, Proceedings. Part II
- Author / Creator
- Conferences
- Mexican International Conference on Artificial Intelligence (20th : 2021 : Mexico City, Mexico)
- Available as
- Online
- Summary
-
The two-volume set LNAI 13067 and 13068 constitutes the proceedings of the 20th Mexican International Conference on Artificial Intelligence, MICAI 2021, held in Mexico City, Mexico, in October 2021...
The two-volume set LNAI 13067 and 13068 constitutes the proceedings of the 20th Mexican International Conference on Artificial Intelligence, MICAI 2021, held in Mexico City, Mexico, in October 2021. The total of 58 papers presented in these two volumes was carefully reviewed and selected from 129 submissions. The first volume, Advances in Computational Intelligence, contains 30 papers structured into three sections: Machine and Deep Learning Image Processing and Pattern Recognition Evolutionary and Metaheuristic Algorithms The second volume, Advances in Soft Computing, contains 28 papers structured into two sections: Natural Language Processing Intelligent Applications and Robotics.
Details
- Creator
- Ildar Batyrshin, Alexander Gelbukh, Grigori Sidorov (eds.)
- Format
- Books
- Language
- English
- Contributors
- Publication
-
- Cham : Springer, 2021
- Series
- Physical Details
-
- 1 online resource (380 pages)
- data file
- ISBNs
- 9783030898205, 3030898202, 3030898199, 9783030898199, 9783030898199
- OCLC
- on1281957733
- Includes index.
- Intro -- Preface -- Conference Organization -- Contents -- Part II -- Contents -- Part I -- Natural Language Processing Supervised -- Supervised Machine Learning for Automatic Assessment of Free-Text Answers -- 1 Introduction -- 2 Related Work -- 3 Our Methodology -- 4 Experimental Results -- 4.1 RQ1 Coverage: How Many Answers Can Be Automatically Assessed? -- 4.2 RQ2 Accuracy: How Accurate Is the Suggested Assessment? -- 5 Conclusion and Future Work -- References -- Towards Multilingual Image Captioning Models that Can Read -- 1 Introduction -- 2 State of the Art
- 2.1 Summary of the State-of-the-Art -- 3 Data and Methods -- 3.1 TextCaps Dataset -- 3.2 Automatic Translation of TextCaps -- 3.3 M4C-Captioner -- 3.4 Multilingual M4C-Captioner -- 3.5 Experimental Setup -- 4 Results -- 5 Conclusions and Future Work -- 6 Declarations -- References -- Best Paper Award, Second Place -- Question Answering for Visual Navigation in Human-Centered Environments -- 1 Introduction -- 2 Related Works -- 3 HISNav VQA Dataset -- 3.1 Images -- 3.2 Human-Asked Questions -- 3.3 Synthetic Questions -- 3.4 Dataset Analysis -- 4 Vector Semiotic Architecture Baseline
- 5 Experiments -- 5.1 Neural Network Baseline -- 5.2 Vector Semiotic Architecture -- 6 Discussion -- 7 Conclusion -- A Appendix: Data Labeling -- B Appendix: Examples -- References -- Improving a Conversational Speech Recognition System Using Phonetic and Neural Transcript Correction -- 1 Introduction -- 2 Background -- 3 Approach and Implementation -- 4 NLU Preprocessing Module -- 4.1 Phonetic Correction -- 4.2 Neural Classification Module -- 5 Experiments -- 6 Results and Discussion -- 7 Conclusions and Future Work -- References
- Estimation of Imageability Ratings of English Words Using Neural Networks -- 1 Introduction -- 2 Data and Method -- 3 Result -- 4 Result Interpretation -- 5 Conclusion -- References -- Hypernyms-Based Topic Discovery Using LDA -- 1 Introduction -- 2 Preliminaries -- 2.1 Latent Dirichlet Allocation -- 2.2 Latent Semantic Analysis -- 2.3 Probabilistic Latent Semantic Analysis -- 2.4 WordNet -- 3 Related Work -- 4 The Proposed Method -- 4.1 Datasets -- 5 Results -- 5.1 Experimental Results -- 6 Conclusions -- References -- Virality Prediction for News Tweets Using RoBERTa -- 1 Introduction
- 2 Related Works -- 3 Our Method -- 3.1 Corpus -- 3.2 Tweet Influence Formula -- 3.3 Method Overview -- 3.4 Method Description -- 4 Results and Discussion -- 4.1 Results -- 4.2 Findings -- 5 Conclusion -- References -- Sentiment Analysis on Twitter About COVID-19 Vaccination in Mexico -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Data Extraction -- 3.2 Data Transformation and Export Resulting Dataset -- 3.3 Data Labeling -- 3.4 Text Processing and Feature Extraction -- 3.5 Machine Learning Models -- 3.6 Experiments -- 3.7 Machine Learning Model Selection -- 4 Results and Discussion -- 5 Conclusions