Results 81 to 90 of about 1,159,266 (295)
Unsupervised Multilingual Word Embeddings [PDF]
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, 2018 EMNLP ...
Xilun Chen 0002, Claire Cardie
openaire +2 more sources
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
Christof Schöch lectures on the Use and Abuse of Word Embedding
, 2020 A blog post reporting on a lecture about the use of word embeddings in ...
KAMLOVSKAYA, Ekaterina
core +1 more source
A supervised topic embedding model and its application.
PLoS ONE, 2022 We propose rTopicVec, a supervised topic embedding model that predicts response variables associated with documents by analyzing the text data. Topic modeling leverages document-level word co-occurrence patterns to learn latent topics of each document ...
Weiran Xu, Koji Eguchi
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
Jurnal LebesgueWord embedding is a technique for converting a word into a vector. These are known as word vectors. Despite the fact that word embedding offers multiple powerful approaches, these existing methods can yet be improved.
Andzar Tsaqif Laksana +2 more
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Toward the Development of Large-Scale Word Embedding for Low-Resourced Language
IEEE Access, 2022 Word embedding is possessed by Natural language processing as a key procedure for semantically and syntactically manipulating the unlabeled text corpus.
Shahzad Nazir +5 more
doaj +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
All Word Embeddings from One Embedding
CoRR, 2020 NeurIPS ...
Sho Takase, Sosuke Kobayashi
openaire +4 more sources
A Lightweight Procedural Layer for Hybrid Experimental–Computational Workflows in Materials Science
Advanced Engineering Materials, EarlyView.We unveil a prototype hybrid‐workflow framework that fuses automatedcomputation with hands‐on experiments. Built atop pyiron, a lightweight, parameterized layer translates procedure descriptions into executable manual steps, syncing instrument settings, human interventions, and data capture in real‐time today.
Steffen Brinckmann +8 more
wiley +1 more source