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1
Language Models Explain Word Reading Times Better Than Empirical Predictability ...
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2
Language Models Explain Word Reading Times Better Than Empirical Predictability
In: Front Artif Intell (2022)
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3
Meditation affects word recognition of meditation novices
In: Psychol Res (2021)
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4
Zen meditation neutralizes emotional evaluation, but not implicit affective processing of words ...
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5
Zen meditation neutralizes emotional evaluation, but not implicit affective processing of words ...
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6
Individual corpora predict fast memory retrieval during reading ...
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7
Individual corpora predict fast memory retrieval during reading ...
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8
A Lexical Frequency Analysis of Irish Sign Language
In: Other Resources (2020)
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9
A Lexical Frequency Analysis of Irish Sign Language
In: Articles (2020)
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10
Zen meditation neutralizes emotional evaluation, but not implicit affective processing of words
Lusnig, Larissa; Radach, Ralph; Mueller, Christina J.. - : Public Library of Science, 2020
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11
Decomposing predictability: Semantic feature overlap between words and the dynamics of reading for meaning ...
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12
Sampling Techniques to Overcome Class Imbalance in a Cyberbullying Context
Hofmann, Markus; Colton, David. - : Universitat Politècnica de València, 2019
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13
A qualitative analysis of the Wikipedia N-Substate Algorithm's Enhancement Terms
Goslin, Kyle; Hofmann, Markus. - : Universitat Politècnica de València, 2019
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14
Non-Manual Articulators in Irish Sign Language Verbs: An Analysis with Data Mining Association Rules
In: Conference Papers (2018)
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15
Typography and individual experience in digital reading: Do readers’ eye movements adapt to poor justification?
Jarosch, Julian [Verfasser]; Schlesewsky, Matthias [Verfasser]; Füssel, Stephan [Verfasser]. - Mannheim : Leibniz-Institut für Deutsche Sprache (IDS), Bibliothek, 2017
DNB Subject Category Language
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16
Taking typography to experimental testing: On the influence of serifs, fonts and justification on eye movements in text reading
Jarosch, Julian [Verfasser]; Schlesewsky, Matthias [Verfasser]; Füssel, Stephan [Verfasser]. - Mannheim : Leibniz-Institut für Deutsche Sprache (IDS), Bibliothek, 2017
DNB Subject Category Language
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17
The two sides of prediction error in reading: on the relationship between eye movements and the N400 in sentence processing
Kretzschmar, Franziska [Verfasser]; Alday, Phillip M. [Verfasser]; Radach, Ralph [Herausgeber]. - Mannheim : Leibniz-Institut für Deutsche Sprache (IDS), Bibliothek, 2017
DNB Subject Category Language
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18
When readers pay attention to the left: A concurrent eyetracking-fMRI investigation on the neuronal correlates of regressive eye movements during reading
Weiß, Anna Fiona [Verfasser]; Kretzschmar, Franziska [Verfasser]; Nagels, Arne [Verfasser]. - Mannheim : Leibniz-Institut für Deutsche Sprache (IDS), Bibliothek, 2017
DNB Subject Category Language
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19
Sentiment Analysis: Comparative Analysis Of Multilingual Sentiment And Opinion Classification Techniques ...
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20
Sentiment Analysis: Comparative Analysis Of Multilingual Sentiment And Opinion Classification Techniques ...
Abstract: Sentiment analysis and opinion mining have become emerging topics of research in recent years but most of the work is focused on data in the English language. A comprehensive research and analysis are essential which considers multiple languages, machine translation techniques, and different classifiers. This paper presents, a comparative analysis of different approaches for multilingual sentiment analysis. These approaches are divided into two parts: one using classification of text without language translation and second using the translation of testing data to a target language, such as English, before classification. The presented research and results are useful for understanding whether machine translation should be used for multilingual sentiment analysis or building language specific sentiment classification systems is a better approach. The effects of language translation techniques, features, and accuracy of various classifiers for multilingual sentiment analysis is also discussed in this study. ... : {"references": ["Bo Pang, Lillian Lee, and Shivakumar Vaithyanathan. Thumbs\nup? Sentiment Classification using Machine Learning Techniques.\nProceedings of the ACL-02 conference on Empirical methods in natural\nlanguage processing - EMNLP, pages 79\u201386, 2002.", "Peter D Turney. Thumbs up or thumbs down? Semantic Orientation\napplied to Unsupervised Classification of Reviews. Proceedings of the\n40th Annual Meeting of the Association for Computational Linguistics\n(ACL), (July):417\u2013424, 2002.", "Andrew B Xiaojin. Introduction to Semi-Supervised Learning. Synthesis\nLectures on Artificial Intelligence and Machine Learning, pages 1\u2013130,\n2009.", "Xiaowen Ding, Xiaowen Ding, Bing Liu, Bing Liu, Philip S. Yu, and\nPhilip S. Yu. A holistic lexicon-based approach to opinion mining.\nProceedings of the international conference on Web search and web\ndata mining - WSDM, page 231, 2008.", "Kevin Hsin Yih Lin, Changhua Yang, and Hsin Hsi Chen. Emotion\nclassification of online news articles from the ...
Keyword: Cross-language analysis; machine learning; machine translation; sentiment analysis.
URL: https://dx.doi.org/10.5281/zenodo.1130529
https://zenodo.org/record/1130529
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