Results 31 to 40 of about 10,729 (237)
Applied Sciences, 2021 Biomedical terms extracted using Word2vec, the most popular word embedding model in recent years, serve as the foundation for various natural language processing (NLP) applications, such as biomedical information retrieval, relation extraction, and ...
Ziheng Zhang +4 more
doaj +1 more source
Decentralized Word2Vec Using Gossip Learning
the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa 2021), 2021 QC ...
Alkathiri, Abdul Aziz +3 more
openaire +5 more sources
dataset-word2vec features.xlsx
, 2022 Word2vec feature ...
Shahid Iqbal (13129134)
core +1 more source
Modeling Musical Context with Word2vec
CoRR, 2017 Proceedings of the First International Conference on Deep Learning and Music, Anchorage, US, May, 2017 (arXiv:1706.08675v1 [cs.NE])
Dorien Herremans, Ching-Hua Chuan
openaire +3 more sources
HOAX DETECTION IN INDONESIA LANGUAGE USING LONG SHORT-TERM MEMORY MODEL
Jurnal Ilmiah SINERGI, 2020 Nowadays, the internet and social media grow fast. This condition has positive and negative effects on society. They become media to communicate and share information without limitation.
Andi Apriliyanto, Retno Kusumaningrum
doaj +1 more source
Temporal Analysis on Topics Using Word2Vec
CoRR, 2022 The present study proposes a novel method of trend detection and visualization - more specifically, modeling the change in a topic over time. Where current models used for the identification and visualization of trends only convey the popularity of a singular word based on stochastic counting of usage, the approach in the present study illustrates the ...
Sandhu, Angad +4 more
openaire +2 more sources
The parameter setting of word2vec.
, 2022 The parameter setting of word2vec.
Yilan Qi (12152837), Jun He (46556)
core +1 more source
Models of lexical semantics in the algorithms for natural language processing [PDF]
Zbornik Radova Filozofskog Fakulteta u Prištini, 2021 The aim of this study was to determine whether some of the approaches of lexical semantics for studying word meaning could be identified in word2vec and recurrent neural networks (RNN), the algorithms for natural language processing (NLP).
Dilparić Branislava M. +1 more
doaj +1 more source
Word2vec Conjecture and A Limitative Result
CoRR, 2020 Being inspired by the success of \texttt{word2vec} \citep{mikolov2013distributed} in capturing analogies, we study the conjecture that analogical relations can be represented by vector spaces. Unlike many previous works that focus on the distributional semantic aspect of \texttt{word2vec}, we study the purely \emph{representational} question: can \emph{
openaire +2 more sources