Results 1 to 10 of about 422,941 (239)

Mind the semantic gap: semantic efficiency in human computer interfaces [PDF]

Frontiers in Artificial Intelligence
As we become increasingly dependent on technology in our daily lives, the usability of HCIs is a key driver of individual empowerment for us all. A primary focus of AI systems has been to make HCIs easier to use by identifying what users need and ...
James Horsley
doaj   +4 more sources

Exploring the Semantic Gap for Movie Recommendations [PDF]

Proceedings of the Eleventh ACM Conference on Recommender Systems, 2017
In the last years, there has been much attention given to the semantic gap problem in multimedia retrieval systems. Much effort has been devoted to bridge this gap by building tools for the extraction of high-level, semantics-based features from ...
Bakhshandegan Moghaddam, Farshad, Cella, Leonardo, Cereda, Stefano, Cremonesi, Paolo, Deldjoo, Yashar, Elahi, Mehdi   +5 more
core   +4 more sources

semantic account of the stative adverb gap

ZAS Papers in Linguistics, 2000
It is argued that there is a surprising gap in the distribution of adverbial modifiers, namely that there are (practically) no adverbs that modify exclusively stative verbs. Given the general range of selectional restrictions associated with adverb/verb modification, this comes as a surprise.
Graham Katz
openaire   +5 more sources

Speakers Fill Lexical Semantic Gaps with Context [PDF]

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2020
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Pimentel, Tiago, Hall Maudslay, Rowan, Blasi, Damián, Cotterell, Ryan   +3 more
openaire   +5 more sources

Rewrite Caption Semantics: Bridging Semantic Gaps for Language-Supervised Semantic Segmentation [PDF]

arXiv, 2023
Vision-Language Pre-training has demonstrated its remarkable zero-shot recognition ability and potential to learn generalizable visual representations from language supervision. Taking a step ahead, language-supervised semantic segmentation enables spatial localization of textual inputs by learning pixel grouping solely from image-text pairs ...
Xing, Yun, Kang, Jian, Xiao, Aoran, Nie, Jiahao, Shao, Ling, Lu, Shijian   +5 more
arxiv   +3 more sources

Mind the Gap: Another look at the problem of the semantic gap in image retrieval [PDF]

SPIE Proceedings, 2006
This paper attempts to review and characterise the problem of the semantic gap in image retrieval and the attempts being made to bridge it. In particular, we draw from our own experience in user queries, automatic annotation and ontological techniques ...
Enser, Peter G. B., Hare, Jonathon S., Lewis, Paul H., Sandom, Christine J.   +3 more
core   +3 more sources

Handling Ontology Gaps in Semantic Parsing [PDF]

Proceedings of the 13th Joint Conference on Lexical and Computational Semantics (*SEM 2024)
The majority of Neural Semantic Parsing (NSP) models are developed with the assumption that there are no concepts outside the ones such models can represent with their target symbols (closed-world assumption). This assumption leads to generate hallucinated outputs rather than admitting their lack of knowledge.
Andrea Bacciu, Marco Damonte, Marco Basaldella, Emilio Monti   +3 more
openaire   +4 more sources

Bridging the gap between social tagging and semantic annotation: E.D. the Entity Describer [PDF]

, 2007
Semantic annotation enables the development of efficient computational methods for analyzing and interacting with information, thus maximizing its value.
Benjamin M. Good, Edward A. Kawas, Mark D. Wilkinson   +2 more
core   +3 more sources

RNG: Reducing Multi-level Noise and Multi-grained Semantic Gap for Joint Multimodal Aspect-Sentiment Analysis [PDF]

arXiv
As an important multimodal sentiment analysis task, Joint Multimodal Aspect-Sentiment Analysis (JMASA), aiming to jointly extract aspect terms and their associated sentiment polarities from the given text-image pairs, has gained increasing concerns. Existing works encounter two limitations: (1) multi-level modality noise, i.e., instance- and feature ...
Yaxin Liu, Yan Zhou, Ziming Li, Jinchuan Zhang, Yu Shang, Chenyang Zhang, Songlin Hu   +6 more
arxiv   +3 more sources

Novel cross-dimensional coarse-fine-grained complementary network for image-text matching [PDF]

PeerJ Computer Science
The fundamental aspects of multimodal applications such as image-text matching, and cross-modal heterogeneity gap between images and texts have always been challenging and complex.
Meizhen Liu, Anis Salwa Mohd Khairuddin, Khairunnisa Hasikin, Weitong Liu   +3 more
doaj   +3 more sources