Results 81 to 90 of about 11,306 (258)
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
Advanced Engineering Materials, EarlyView.A unified research data management framework for heterogeneous materials data is presented. The system integrates multimodal datasets using ontologies and knowledge graphs, enabling interoperability and FAIR (findable, accessible, interoperable, reusable) data principles. By linking data across scales and workflows, it supports reproducible, Artifitial
Doaa Mohamed +6 more
wiley +1 more source
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
Fostering Innovation: Streamlining Magnetocaloric Materials Research by Digitalization
Advanced Engineering Materials, EarlyView.Magnetocaloric cooling (MCE) is an environmentally friendly refrigeration method with great potential. Optimizing MCE materials involves the preparation and screening of large quantities of samples, which in turn generates a large amount of data. A digitalization approach is presented that uses ontologies, knowledge graphs, and digital workflows to ...
Simon Bekemeier +17 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
Evaluation of acoustic word embeddings [PDF]
Proceedings of the 1st Workshop on Evaluating Vector-Space
Representations for NLP, 2016 Recently, researchers in speech recognition have started to reconsider using whole words as the basic modeling unit, instead of phonetic units. These systems rely on a function that embeds an arbitrary or fixed dimensional speech segments to a vector in a fixed-dimensional space, named acoustic word embedding.
Sahar Ghannay +3 more
openaire +1 more source
Advanced Engineering Materials, EarlyView.Building machine‐readable vocabularies for materials science is slow, expert‐driven work. This study benchmarks 13 large language models on two of its first steps: finding candidate terms in engineering articles and deciding where they belong in a class hierarchy.
Thomas Bjarsch +3 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
Multimodal Data‐Driven Microstructure Characterization
Advanced Engineering Materials, EarlyView.A self‐consistent autonomous workflow for EBSP‐based microstructure segmentation by integrating PCA, GMM clustering, and cNMF with information‐theoretic parameter selection, requiring no user input. An optimal ROI size related to characteristic grain size is identified.
Qi Zhang +4 more
wiley +1 more source
Microstructure Evolution of a VMnFeCoNi High‐Entropy Alloy After Synthesis, Swaging, and Annealing
Advanced Engineering Materials, EarlyView.The synthesis and processing (rotary swaging and annealing) of the novel VMnFeCoNi alloy is investigated, alongside the estimation of the grain size effect on hardness. Analysis of a wide grain size range of recrystallized microstructures (12–210 µm) reveals a low annealing twin density.
Aditya Srinivasan Tirunilai +6 more
wiley +1 more source