Results 21 to 30 of about 6,791 (190)
Study on Title Encoding Methods for e-Commerce Downstream Tasks
Proceedings of the International Florida Artificial Intelligence Research Society Conference, 2022 In an e-Commerce marketplace there are usually many downstream tasks which have (relatively) less available resources than the few mainstream priority tasks, like recommendation or search.
Cristian Cardellino, Rafael Carrascosa
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Large scale text mining for deriving useful insights: A case study focused on microbiome
Frontiers in Physiology, 2022 Text mining has been shown to be an auxiliary but key driver for modeling, data harmonization, and interpretation in bio-medicine. Scientific literature holds a wealth of information and embodies cumulative knowledge and remains the core basis on which ...
Syed Ashif Jardary Al Ahmed +8 more
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Scaling Word2Vec on Big Corpus [PDF]
Data Science and Engineering, 2019 Abstract Word embedding has been well accepted as an important feature in the area of natural language processing (NLP). Specifically, the Word2Vec model learns high-quality word embeddings and is widely used in various NLP tasks. The training of Word2Vec is sequential on a CPU due to strong dependencies between word–context pairs.
Bofang Li +5 more
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Information, 2020 The ability to stop malware as soon as they start spreading will always play an important role in defending computer systems. It must be a huge benefit for organizations as well as society if intelligent defense systems could themselves detect and ...
Kien Tran, Hiroshi Sato, Masao Kubo
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Frontiers in Genetics, 2021 Ion channels are the second largest drug target family. Ion channel dysfunction may lead to a number of diseases such as Alzheimer’s disease, epilepsy, cephalagra, and type II diabetes.
Jie Zheng, Xuan Xiao, Wang-Ren Qiu
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Information, 2020 Mobile applications (apps) on IOS and Android devices are mostly maintained and updated via Apple Appstore and Google Play, respectively, where the users are allowed to provide reviews regarding their satisfaction towards particular apps.
Xiaozhou Li +3 more
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Efficient Parallel Learning of Word2Vec [PDF]
CoRR, 2016 Since its introduction, Word2Vec and its variants are widely used to learn semantics-preserving representations of words or entities in an embedding space, which can be used to produce state-of-art results for various Natural Language Processing tasks.
Vuurens, J.B.P. +2 more
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Natural Language Engineering, 2016 AbstractMy last column ended with some comments about Kuhn and word2vec. Word2vec has racked up plenty of citations because it satisifies both of Kuhn’s conditions for emerging trends: (1) a few initial (promising, if not convincing) successes that motivate early adopters (students) to do more, as well as (2) leaving plenty of room for early adopters ...
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Mathematics, 2023 Personalized recommender systems are used not only in e-commerce companies but also in various web applications. These systems conventionally use collaborative filtering (CF) and content-based filtering approaches. CF operates using memory-based or model-
Yong Eui Kim +4 more
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CoRR, 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. State-of-the-art performance is also provided by skip-gram with negative-sampling (SGNS) implemented in the word2vec tool. In this note, we explain the similarities
Tianze Shi, Zhiyuan Liu 0001
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