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201002s2020 sz o 101 0 eng d |
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|a 1202465004
|a 1264825750
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|a 9783030590826
|q (electronic bk.)
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|2 doi
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|a (OCoLC)1198558227
|z (OCoLC)1202465004
|z (OCoLC)1264825750
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|a com.springer.onix.9783030590826
|b Springer Nature
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|a QA76.9.N38
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| 049 |
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|a HCDD
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|a Conference on Artificial Intelligence and Natural Language
|n (9th :
|d 2020 :
|c Online)
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| 245 |
1 |
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|a Artificial intelligence and natural language :
|b 9th Conference, AINL 2020, Helsinki, Finland, October 7-9, 2020, Proceedings /
|c Andrey Filchenkov, Janne Kauttonen, Lidia Pivovarova (eds.).
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3 |
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|a AINL 2020
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| 260 |
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|a Cham, Switzerland :
|b Springer,
|c 2020.
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| 300 |
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|a 1 online resource
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| 336 |
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|a text
|b txt
|2 rdacontent
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| 337 |
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|a computer
|b c
|2 rdamedia
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| 338 |
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|a online resource
|b cr
|2 rdacarrier
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| 490 |
1 |
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|a Communications in computer and information science,
|x 1865-0929 ;
|v 1292
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| 500 |
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|a International conference proceedings.
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| 500 |
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|a "Originally planned to take place at Helsinki in Finland, AINL 2020 was held as a fully digital conference during October 7-9."
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| 500 |
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|a Includes author index.
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| 588 |
0 |
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|a Online resource; title from PDF title page (SpringerLink, viewed November 24, 2020).
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| 505 |
0 |
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|a Intro -- Preface -- Organization -- Contents -- PolSentiLex: Sentiment Detection in Socio-Political Discussions on Russian Social Media -- 1 Introduction -- 2 Related Work -- 2.1 Sentiment Analysis in the Russian Langauge -- 3 PolSentiLex -- 3.1 LiveJournal Collection of Social and Political Posts -- 3.2 Selection of Potentially Sentiment-Bearing Words -- 3.3 Data Mark Up -- 3.4 The Three Versions of PolSentiLex -- 4 PolSentiLex Quality Assessment -- 4.1 Datasets -- 5 Results -- 6 Conclusion -- References
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| 505 |
8 |
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|a Automatic Detection of Hidden Communities in the Texts of Russian Social Network Corpus -- Abstract -- 1 Introduction -- 2 Related Works -- 3 Experiments with the Russian Corpus of VKontakte Posts -- 3.1 Corpus Collecting and Preprocessing -- 3.2 Author-Topic Models -- 3.3 Automatic Labeling of Topics -- 3.4 Model of Hidden Communities in VKontakte Social Network -- 4 Results and Evaluation -- 5 Summary -- References -- Dialog Modelling Experiments with Finnish One-to-One Chat Data -- 1 Introduction -- 2 Related Work -- 2.1 Language Resources for One-to-One Chat Dialogue Data
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| 505 |
8 |
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|a 2.2 Machine Learning for Chat Data Modelling -- 2.3 Evaluation Results for Chat Data Models -- 3 Experimental Setting -- 3.1 Description of the Data Sets -- 3.2 Implementation and Parameters of Methods -- 3.3 Data Preprocessing -- 4 Results -- 4.1 Output Examples -- 5 Analysis and Discussion -- 6 Conclusions -- References -- Advances of Transformer-Based Models for News Headline Generation -- 1 Introduction -- 2 Related Work -- 3 Models Description -- 4 Datasets -- 5 Experiments -- 5.1 Evaluation -- 5.2 Training Dynamics -- 6 Results -- 6.1 Human Evaluation -- 6.2 Error Analysis
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8 |
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|a 7 Conclusion and Future Work -- References -- An Explanation Method for Black-Box Machine Learning Survival Models Using the Chebyshev Distance -- 1 Introduction -- 2 Basic Definitions of Survival Analysis -- 3 LIME -- 4 A General Algorithm of SurvLIME and SurvLIME-Inf -- 5 Optimization Problem for Computing Parameters -- 6 Numerical Experiments -- 6.1 Synthetic Data -- 6.2 Real Data -- 7 Conclusion -- References -- Unsupervised Neural Aspect Extraction with Related Terms -- 1 Introduction -- 2 Related Work -- 3 The Proposal -- 3.1 Model -- 3.2 Training Objective -- 4 Experiments -- 4.1 Datasets
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| 505 |
8 |
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|a 4.2 Experimental Settings -- 4.3 Evaluation Settings -- 4.4 Aspect Extraction Results -- 4.5 Aspect and Aspect Term Extraction Results -- 5 Conclusions -- References -- Predicting Eurovision Song Contest Results Using Sentiment Analysis -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Collection of Eurovision Tweets -- 3.2 Identification of the Source Country -- 3.3 Tweet Tokenization -- 3.4 Identification of the Target Country -- 3.5 Sentiment Analysis -- 3.6 Tallying of Final Results -- 4 Experimental Results -- 4.1 Televoting Algorithm -- 4.2 Different Sampling Windows
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| 650 |
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0 |
|a Natural language processing (Computer science)
|v Congresses.
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| 650 |
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0 |
|a Artificial intelligence
|v Congresses.
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| 650 |
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7 |
|a Artificial intelligence
|2 fast
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| 650 |
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7 |
|a Natural language processing (Computer science)
|2 fast
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| 655 |
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7 |
|a proceedings (reports)
|2 aat
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| 655 |
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|a Conference papers and proceedings
|2 fast
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| 655 |
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|a Conference papers and proceedings.
|2 lcgft
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| 655 |
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7 |
|a Actes de congrès.
|2 rvmgf
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| 700 |
1 |
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|a Filchenkov, Andrey.
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| 700 |
1 |
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|a Kauttonen, Janne.
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| 700 |
1 |
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|a Pivovarova, Lidia.
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| 776 |
0 |
8 |
|i Print version:
|z 303059081X
|z 9783030590819
|w (OCoLC)1182852116
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| 830 |
|
0 |
|a Communications in computer and information science ;
|v 1292.
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| 856 |
4 |
0 |
|u https://holycross.idm.oclc.org/login?auth=cas&url=https://link.springer.com/10.1007/978-3-030-59082-6
|y Click for online access
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| 903 |
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|a SPRING-COMP2020
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| 994 |
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|a 92
|b HCD
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