Zobrazeno 1 - 10
of 285
pro vyhledávání: '"Latecki, Longin Jan"'
Autor:
Zhang, Qi, Chen, Zhijia, Pan, Huitong, Caragea, Cornelia, Latecki, Longin Jan, Dragut, Eduard
Scientific information extraction (SciIE) is critical for converting unstructured knowledge from scholarly articles into structured data (entities and relations). Several datasets have been proposed for training and validating SciIE models. However,
Externí odkaz:
http://arxiv.org/abs/2410.21155
Flowcharts are graphical tools for representing complex concepts in concise visual representations. This paper introduces the FlowLearn dataset, a resource tailored to enhance the understanding of flowcharts. FlowLearn contains complex scientific flo
Externí odkaz:
http://arxiv.org/abs/2407.05183
Publikováno v:
LREC-COLING. (2024) 14407-14417
We present SciDMT, an enhanced and expanded corpus for scientific mention detection, offering a significant advancement over existing related resources. SciDMT contains annotated scientific documents for datasets (D), methods (M), and tasks (T). The
Externí odkaz:
http://arxiv.org/abs/2406.14756
Autor:
Almalki, Amani, Latecki, Longin Jan
The computer-assisted radiologic informative report has received increasing research attention to facilitate diagnosis and treatment planning for dental care providers. However, manual interpretation of dental images is limited, expensive, and time-c
Externí odkaz:
http://arxiv.org/abs/2306.10623
Publikováno v:
Transactions of the Association for Computational Linguistics. 11 (2023) 1132-1146
The recognition of dataset names is a critical task for automatic information extraction in scientific literature, enabling researchers to understand and identify research opportunities. However, existing corpora for dataset mention detection are lim
Externí odkaz:
http://arxiv.org/abs/2305.11779
Due to a huge volume of information in many domains, the need for classification methods is imperious. In spite of many advances, most of the approaches require a large amount of labeled data, which is often not available, due to costs and difficulti
Externí odkaz:
http://arxiv.org/abs/2304.12492
Impressive advances in acquisition and sharing technologies have made the growth of multimedia collections and their applications almost unlimited. However, the opposite is true for the availability of labeled data, which is needed for supervised tra
Externí odkaz:
http://arxiv.org/abs/2304.12448
Autor:
Hanif, Sidra, Latecki, Longin Jan
In recent years, there is a growing number of pre-trained models trained on a large corpus of data and yielding good performance on various tasks such as classifying multimodal datasets. These models have shown good performance on natural images but
Externí odkaz:
http://arxiv.org/abs/2212.00847
Autor:
Almalki, Amani, Latecki, Longin Jan
The computer-assisted radiologic informative report is currently emerging in dental practice to facilitate dental care and reduce time consumption in manual panoramic radiographic interpretation. However, the amount of dental radiographs for training
Externí odkaz:
http://arxiv.org/abs/2210.11404
The recent advances of compressing high-accuracy convolution neural networks (CNNs) have witnessed remarkable progress for real-time object detection. To accelerate detection speed, lightweight detectors always have few convolution layers using singl
Externí odkaz:
http://arxiv.org/abs/2209.13933