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The Spectral Underpinning of word2vec [PDF]
Frontiers in Applied Mathematics and Statistics, 2020 Word2vec introduced by Mikolov et al. is a word embedding method that is widely used in natural language processing. Despite its success and frequent use, a strong theoretical justification is still lacking.
Ariel Jaffe +7 more
doaj +4 more sources
Considerations about learning Word2Vec [PDF]
Journal of Supercomputing, 2021 AbstractDespite the large diffusion and use of embedding generated through Word2Vec, there are still many open questions about the reasons for its results and about its real capabilities. In particular, to our knowledge, no author seems to have analysed in detail how learning may be affected by the various choices of hyperparameters.
Giovanni Di Gennaro +2 more
exaly +3 more sources
Linking GloVe with word2vec [PDF]
, 2014 The Global Vectors for word representation (GloVe), introduced by Jeffrey Pennington et al. is reported to be an efficient and effective method for learning vector representations of words.
Liu, Zhiyuan, Shi, Tianze
core +2 more sources
Determining the Characteristic Vocabulary for a Specialized Dictionary using Word2vec and a Directed Crawler [PDF]
, 2016 Specialized dictionaries are used to understand concepts in specific domains, especially where those concepts are not part of the general vocabulary, or having meanings that differ from ordinary languages.
Grefenstette, Gregory, Muchemi, Lawrence
core +6 more sources
Word2vec convolutional neural networks for classification of news articles and tweets
PLoS ONE, 2019 Big web data from sources including online news and Twitter are good resources for investigating deep learning. However, collected news articles and tweets almost certainly contain data unnecessary for learning, and this disturbs accurate learning.
Beakcheol Jang, Jong Wook Kim
exaly +2 more sources
Fast2Vec, a modified model of FastText that enhances semantic analysis in topic evolution [PDF]
PeerJ Computer ScienceBackground Topic modeling approaches, such as latent Dirichlet allocation (LDA) and its successor, the dynamic topic model (DTM), are widely used to identify specific topics by extracting words with similar frequencies from documents.
Ayu Pertiwi, Azhari Azhari, Sri Mulyana
doaj +3 more sources
Central Kurdish Sentiment Analysis Using Deep Learning [PDF]
مجلة جامعة الانبار للعلوم الصرفة, 2022 Sentiment Analysis (SA) as a type of opinion mining and as a more general topic than polarity detection, is widely used for analyzing user's reviews or comments of online expressions, which is implemented using various techniques among which the ...
Kozhin Awlla, Hadi Veisi
doaj +1 more source
Subjective Answers Evaluation Using Machine Learning and Natural Language Processing
IEEE Access, 2021 Subjective paper evaluation is a tricky and tiresome task to do by manual labor. Insufficient understanding and acceptance of data are crucial challenges while analyzing subjective papers using Artificial Intelligence (AI).
Muhammad Farrukh Bashir +4 more
doaj +1 more source
Prediction of Piwi-Interacting RNAs and Their Functions via Convolutional Neural Network
IEEE Access, 2021 In eukaryotic cells, Piwi-interacting RNAs (piRNAs) are the type of short chain non-coding RNA molecules, which interconnect with PIWI proteins. It performs various cellular and genetic functions such as gene-specific protein translation, expression ...
Muhammad Tahir +3 more
doaj +1 more source
Proceedings of the 28th ACM International Conference on Information and Knowledge Management, 2019 The \em word2vec methodology such as Skip-gram and CBOW has seen significant interest in recent years because of its ability to model semantic notions of word similarity and distances in sentences. A related methodology, referred to as \em doc2vec is also able to embed sentences and paragraphs. These methodologies, however, lead to different embeddings
Suhang Wang, Charu Aggarwal, Huan Liu
openaire +1 more source