Temat: word2vec - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: A Comparative Analysis of the Semantic Field of ‘deception’: A Case Study Of Russian And American Imageboard Messages
Autorzy:: Lykova, Olga
Gordeev, Denis
Powiązania:: https://bibliotekanauki.pl/articles/2028581.pdf
Data publikacji:: 2021-03-30
Wydawca:: Uniwersytet Łódzki. Wydawnictwo Uniwersytetu Łódzkiego
Tematy:: artificial neural networks
deception
semantic field
anonymity
word2vec
Opis:: This article uses the material of anonymous Internet forums to analyse the semantic field of deception by the instrumentality of artificial neural networks. Two major imageboards were investigated: 2ch.hk and 4chan.org, being the most popular Russian and American imageboards. For the experiment an algorithm called Word2vec was used to examine 30 million word usages for either of the languages. This analysis revealed 10 words with the greatest semantic proximity to terms from semantic fields of «deception» for Russian and American English. The results showed the tendency among native Russian imageboard users to link the concept of deception with religion and spiritual sphere, while American forum users associate deception with politics and related concepts.
Źródło:: Research in Language; 2021, 19, 1; 95-106
1731-7533
Pojawia się w:: Research in Language
Dostawca treści:: Biblioteka Nauki

Artykuł

Skocz do pozycji: 2.

Tytuł:: Towards a new deep learning algorithm based on GRU and CNN: NGRU
Autorzy:: Atassi, Abdelhamid
el Azami, Ikram
Powiązania:: https://bibliotekanauki.pl/articles/2141895.pdf
Data publikacji:: 2020
Wydawca:: Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:: Convolutional Neural Network
CNN
Gated Recurrent Unit
GRU
SemEval
Twitter
word2vec
Keras
TensorFlow
Adadelta
Adam
soft-max
deep learning
Opis:: This paper describes our new deep learning system based on a comparison between GRU and CNN. Initially we start with the first system which uses Convolutional Neural Network (CNN) which we will compare with the second system which uses Gated Recurrent Unit (GRU). And through this comparison we propose a new system based on the positive points of the two previous systems. Therefore, this new system will take the right choice of hyper-parameters recommended by the authors of both systems. At the final stage we propose a method to apply this new system to the dataset of different languages (used especially in socials networks).
Źródło:: Journal of Automation Mobile Robotics and Intelligent Systems; 2020, 14, 4; 45-47
1897-8649
2080-2145
Pojawia się w:: Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:: Biblioteka Nauki

Artykuł

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