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61
An Enhanced Corpus for Arabic Newspapers Comments
In: ISSN: 1683-3198 ; International Arab Journal of Information Technology ; https://hal.archives-ouvertes.fr/hal-03124728 ; International Arab Journal of Information Technology, Colleges of Computing and Information Society (CCIS), 2020, 17 (5), pp.789-798. ⟨10.34028/iajit/17/5/12⟩ (2020)
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62
Sonnet Combinatorics with OuPoCo
In: 4th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature ; https://hal.archives-ouvertes.fr/hal-03084603 ; 4th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, ACL-SIGHUM, Dec 2020, Barcelona, Spain ; https://www.aclweb.org/anthology/volumes/2020.latechclfl-1/ (2020)
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63
Text Analytics ; Text Analytics: Advances and Challenges
Domenica Fioredistella, Iezzi; Mayaffre, Damon; Michelangelo, Misuraca. - : HAL CCSD, 2020. : Springer, 2020
In: https://hal.archives-ouvertes.fr/hal-03099604 ; Springer, 2020, 978-3-030-52679-5 (2020)
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64
NTeALan Dictionaries Platforms: An Example Of Collaboration-Based Model
In: Proceedings of the 1st International Workshop on Language Technology Platforms (IWLTP 2020) ; https://hal.archives-ouvertes.fr/hal-02701912 ; Proceedings of the 1st International Workshop on Language Technology Platforms (IWLTP 2020), 2020, pp.11 - 16 (2020)
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65
Factions: acts of worldbuilding on social media platforms ...
Little, Dana L.. - : University of Glasgow, 2020
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66
Computational Propaganda: Targeted Advertising and the Perception of Truth
In: Conference Papers (2020)
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67
Overcoming Alzheimer’s Disease Stigma by Leveraging Artificial Intelligence and Blockchain Technologies
In: Brain Sciences ; Volume 10 ; Issue 3 (2020)
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68
Conversational Language Learning for Human-Robot Interaction
Bothe, Chandrakant Ramesh. - : Staats- und Universitätsbibliothek Hamburg Carl von Ossietzky, 2020
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69
USING DEEP LEARNING AND LINGUISTIC ANALYSIS TO PREDICT FAKE NEWS WITHIN TEXT
In: Master's Projects (2020)
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70
Comparison of Word2vec with Hash2vec for Machine Translation
In: Master's Projects (2020)
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71
LINGUISTIC AND HUMANITARIAN COMPETENCE OF FUTURE ENGINEERS: THE PHILOSOPHICAL AND ANTHROPOLOGICAL ASPECT
In: Human Studies. Series of Pedagogy; № 10/42 (2020); 122-134 ; Людинознавчі студії. Серія Педагогіка; № 10/42 (2020); 122-134 ; 2413-2039 ; 2313-2094 (2020)
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72
Речевые маркеры обмана в устных ответах Джеймса Коми об использовании ФБР юридического инструмента FISA ; Verbal Markers of Deception in James Comey’s FISA Talk
Кныш, А. Ю.. - : Издательство Уральского университета, 2020
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73
Digital Biomarkers for the Early Detection of Mild Cognitive Impairment: Artificial Intelligence Meets Virtual Reality
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74
Exploring Explicit and Implicit Feature Spaces in Natural Language Processing Using Self-Enrichment and Vector Space Analysis
In: Electronic Thesis and Dissertation Repository (2020)
Abstract: Machine Learning in Natural Language Processing (NLP) deals directly with distributed representations of words and sentences. Words are transformed into vectors of real values, called embeddings, and used as the inputs to machine learning models. These architectures are then used to solve NLP tasks such as Sentiment Analysis and Natural Language Inference. While solving these tasks many models will create word embeddings and sentence embeddings as outputs. We are interested in how we can transform and analyze these output embeddings and modify our models, to both improve the task result and give us an understanding of the spaces. To this end we introduce the notion of explicit features, the actual values of the embeddings, and implicit features, information encoded into the space of vectors by solving the task, and hypothesis on an idealized spaces, where implicit features directly create the explicit features by means of basic linear algebra and set theory. To test if our output spaces are similar to our ideal space we vary the model and, motivated by Transformer architectures, introduce the notion of Self-Enriching layers. We also create idealized spaces, and run task experiments to see if the patterns of results can give us insight into the output spaces, as well we run transfer learning experiments to see what kinds of information are being represented by our models. Finally, we run direct analysis of the vectors of the word and sentence outputs for comparison.
Keyword: Artificial Intelligence and Robotics; distributed representations; natural language inference; natural language processing; sentence embeddings; sentiment analysis; word embeddings
URL: https://ir.lib.uwo.ca/cgi/viewcontent.cgi?article=9954&context=etd
https://ir.lib.uwo.ca/etd/7471
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75
Human-AI Interaction in the Presence of Ambiguity: From Deliberation-based Labeling to Ambiguity-aware AI
Schaekermann, Mike. - : University of Waterloo, 2020
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76
Cognitive discriminative feature selection using variance fractal dimension for the detection of cyber attacks
Kaiser, Samilat. - 2020
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77
Chinese computational linguistics : 18th China National Conference, CCL 2019, Kunming, China, October 18-20, 2019 : proceedings
Liu, Zhiyuan (Herausgeber); Jiang, Heng (Herausgeber); Liu, Yang (Herausgeber). - Cham, Switzerland : Springer, 2019
BLLDB
UB Frankfurt Linguistik
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78
Analogy between concepts
In: ISSN: 0004-3702 ; Artificial Intelligence ; https://hal.inria.fr/hal-02186292 ; Artificial Intelligence, Elsevier, 2019, 275, pp.487-539. ⟨10.1016/j.artint.2019.06.008⟩ ; https://www.sciencedirect.com/science/article/pii/S0004370218301863 (2019)
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79
FEEL: a French Expanded Emotion Lexicon
In: https://hal-lirmm.ccsd.cnrs.fr/lirmm-02136090 ; 2019, ⟨swh:1:dir:35a446bb6f0808aa0db6f5bcd032f43e9ec71591;origin=https://hal.archives-ouvertes.fr/lirmm-02136090;visit=swh:1:snp:9d258bfd8a07f79cb8063b6ac33d6e710bd3e3f3;anchor=swh:1:rev:2266d773cf993b8718a1d7ca1d1bce145118f527;path=/⟩ (2019)
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80
Εntity-level Εvent Ιmpact Αnalytics ; Analyse de l’Impact des Événements au Niveau des Entités
Govind, Govind. - : HAL CCSD, 2019
In: https://hal.archives-ouvertes.fr/tel-02102795 ; Document and Text Processing. Normandie Université, Unicaen, EnsiCaen, CNRS, GREYC UMR 6072, 2019. English (2019)
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