Results 71 to 80 of about 5,349,741 (340)

Semantic Parsing of Interpage Relations

open access: yes2022 26th International Conference on Pattern Recognition (ICPR), 2022
Page-level analysis of documents has been a topic of interest in digitization efforts, and multimodal approaches have been applied to both classification and page stream segmentation. In this work, we focus on capturing finer semantic relations between pages of a multi-page document.
Mehmet Arif Demirtas   +3 more
openaire   +4 more sources

Composite Semantic Relation Classification [PDF]

open access: yes, 2017
Different semantic interpretation tasks such as text entailment and question answering require the classification of semantic relations between terms or entities within text. However, in most cases it is not possible to assign a direct semantic relation between entities/terms.
Siamak Barzegar   +3 more
openaire   +4 more sources

Semantic Relations Between Nominals

open access: yes, 2013
Table of Contents: Introduction / Relations between Nominals, Relations between Concepts / Extracting Semantic Relations with Supervision / Extracting Semantic Relations with Little or No Supervision ...
Nastase, Viviana Antonela   +3 more
core   +1 more source

Representing and Reasoning over Topological Relations in OWL [PDF]

open access: yes
Representing topological information for the Semantic Web often involves qualitative defined natural language terms such as "Into" or "Overlapping". This can be the case when exact coordinates of spatial regions are not available, they are incomplete or
Tachmazidis, Ilias   +2 more
core   +2 more sources

CoSP: Reconfigurable Metamaterial Inverse Design via Contrastive Pretrained Large Language Model

open access: yesAdvanced Science, EarlyView.
In this work, CoSP (contrastive multi‐state pretrain), an intelligent inverse design method for reconfigurable metamaterials based on a contrastive pretrained large language model, is proposed. Numerical experiments demonstrate that CoSP can design reconfigurable metamaterial structures for multi‐state, multi‐band optical responses, showing great ...
Shujie Yang   +4 more
wiley   +1 more source

Cataloging technology of information fund [PDF]

open access: yesКомпьютерные исследования и моделирование, 2015
The article discusses the approach to the improvement of information processing technology on the basis of logical-semantic network (LSN) Question-Answer-Reaction aimed at formation and support of the catalog service providing efficient search of answers
V. N. Dobrynin, I. A. Filozova
doaj   +1 more source

SemanticST: A Scalable Multi‐Contextual Graph Learning Framework for Uncovering Spatial Niches and Robust Multi‐Sample Integration in Spatial Transcriptomics

open access: yesAdvanced Science, EarlyView.
Technical limitations often let dominant signals overshadow rare cell types and fine‐grained heterogeneity in spatial transcriptomics. SemanticST, a graph neural network using multi‐semantic graph fusion and a novel min‐cut loss, recovers these subtle patterns.
Roxana Zahedi   +7 more
wiley   +1 more source

Exploring Vector Spaces for Semantic Relations

open access: yesConference on Empirical Methods in Natural Language Processing, 2017
Word embeddings are used with success for a variety of tasks involving lexical semantic similarities between individual words. Using unsupervised methods and just cosine similarity, encouraging results were obtained for analogical similarities.
Kata Gábor   +4 more
semanticscholar   +1 more source

Derivational and Semantic Relations of Croatian Verbs

open access: yesJournal of Language Modelling, 2012
This paper deals with certain morphosemantic relations between Croatian verbs and discusses their inclusion in Croatian WordNet. The morphosemantic relations in question are the semantic relations between unprefixed infinitives and their prefixed ...
Krešimir Šojat   +2 more
doaj   +1 more source

Learning Work Function via Implicit Reasoning on Electrostatic Potential Landscapes

open access: yesAdvanced Science, EarlyView.
StructPot‐CLR establishes a cross‐modal contrastive learning framework that aligns the crystal structures of 2D materials with plane‐averaged electrostatic potential landscapes for physically informed work‐function prediction. The model achieves an MAE of 0.265 eV and an R2 of 0.902 on the held‐out test set while accurately preserving key morphological
Haoyu Wan, Yue Wu, Tianhao Su, Deng Pan
wiley   +1 more source

Home - About - Disclaimer - Privacy