Results 111 to 120 of about 19,032 (297)
Event-Driven Semantic Service Discovery Based on Word Embeddings
IEEE Access, 2018 Service discovery is vital to event handling in Internet of Things applications which are based on the event-driven service-oriented architecture. However, in service discovery, the problem of service matching that establishes relationships between ...
Fagui Liu +3 more
doaj +1 more source
Advanced Engineering Materials, EarlyView.Composite Ti–6Al–4V–epoxy lattice structures are additively manufactured and epoxy infiltrated for cyclic loading. At low lattice volume fractions, hybridization produces synergistic gains in stiffness and energy dissipation. At higher volume fractions, synergy diminishes, although composites still exceed metallic lattices in specific energy ...
Joey Tallon +3 more
wiley +1 more source
Emotional Embeddings: Refining Word Embeddings to Capture Emotional Content of Words
CoRR, 2019 Word embeddings are one of the most useful tools in any modern natural language processing expert's toolkit. They contain various types of information about each word which makes them the best way to represent the terms in any NLP task. But there are some types of information that cannot be learned by these models.
Armin Seyeditabari +3 more
openaire +2 more sources
Predicting High-Level Human Judgment Across Diverse Behavioral Domains
Collabra: Psychology, 2019 Recent advances in machine learning, combined with the increased availability of large natural language datasets, have made it possible to uncover semantic representations that characterize what people know about and associate with a wide range of ...
Russell Richie +2 more
doaj +1 more source
Advanced Functional Materials, EarlyView.Oxygen‐tunnel (OT) indium tin oxide (ITO) vertical channel transistors (VCTs) enable reliable, high‐density gain‐cell memory for monolithic 3D integration. A sandwiched SiN/SiO2/SiN OT stack selectively regulates oxygen transport, suppressing parasitic electrode oxidation while stabilizing channel oxygen vacancies, thereby suppressing carrier injection
Hyeonho Gu +17 more
wiley +1 more source
Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, 2016 Distilling knowledge from a well-trained cumbersome network to a small one has recently become a new research topic, as lightweight neural networks with high performance are particularly in need in various resource-restricted systems. This paper addresses the problem of distilling word embeddings for NLP tasks.
Lili Mou +5 more
openaire +2 more sources
Investigating Word Meta-Embeddings by Disentangling Common and Individual Information
IEEE Access, 2020 In the field of natural language processing, combining multiple pre-trained word embeddings has become a viable approach to improve word representations. However, there is still a lack of understanding of why such improvements can be achieved.
Wenfan Chen +3 more
doaj +1 more source
Speckle‐Engineered Upconversion Amplification in Nanoemulsion‐Templated Hydrogel Microdomes
Advanced Functional Materials, EarlyView.Nanoemulsion‐confined PEGDA microdomes generate speckle‐like excitation fields that strongly amplify upconversion luminescence upon dehydration, enabling filter‐free visible readout with reversible on–off switching. DMD‐based lithography yields scalable, shape‐programmable arrays for moisture‐responsive displays and optical encryption.
Chaeyeong Ryu +13 more
wiley +1 more source
Word Embeddings for Banking Industry
, 2023 Applications of Natural Language Processing (NLP) are plentiful, from sentiment analysis to text classification. Practitioners rely on static word embeddings (e.g. Word2Vec or GloVe) or static word representation from contextual models (e.g. BERT or ELMo)
Patel, Avnish
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