Results 41 to 50 of about 17,118 (185)
Leveraging Integrated Learning for Open-Domain Chinese Named Entity Recognition
International Journal of Crowd Science, 2022 Named entity recognition (NER) is a fundamental technique in natural language processing that provides preconditions for tasks, such as natural language question reasoning, text matching, and semantic text similarity.
Jin Diao, Zhangbing Zhou, Guangli Shi
doaj +1 more source
, 2011 This paper deals with the identification of Multiword Expressions (MWEs) in Manipuri, a highly agglutinative Indian Language. Manipuri is listed in the Eight Schedule of Indian Constitution.
Bandyopadhyay, Sivaji +1 more
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Scalable Full Flow with Learned Binary Descriptors
, 2017 We propose a method for large displacement optical flow in which local matching costs are learned by a convolutional neural network (CNN) and a smoothness prior is imposed by a conditional random field (CRF).
A Shekhovtsov +8 more
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CRaFT: A Conditional Random Fields Toolbox for Matlab
SoftwareXConditional random fields (CRFs) provide a powerful framework for modeling spatial variability while enforcing consistency with measured data, and are essential for uncertainty analysis across a wide range of engineering applications.
Zhibao Zheng
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Aspect Term Extraction Based on MFE-CRF
Information, 2018 This paper is focused on aspect term extraction in aspect-based sentiment analysis (ABSA), which is one of the hot spots in natural language processing (NLP).
Yanmin Xiang, Hongye He, Jin Zheng
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CONTEXT MODELS FOR CRF-BASED CLASSIFICATION OF MULTITEMPORAL REMOTE SENSING DATA [PDF]
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2012 The increasing availability of multitemporal satellite remote sensing data offers new potential for land cover analysis. By combining data acquired at different epochs it is possible both to improve the classification accuracy and to analyse land cover ...
T. Hoberg, F. Rottensteiner, C. Heipke
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A Conditional Random Field for Multiple-Instance Learning [PDF]
, 2010 We present MI-CRF, a conditional random field (CRF) model for multiple instance learning (MIL). MI-CRF models bags as nodes in a CRF with instances as their states. It combines discriminative unary instance classifiers and pairwise dissimilarity measures.
Deselaers, T., Ferrari, V.
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Advanced Intelligent Systems, EarlyView.Four decades of retinal vessel segmentation research (1982–2025) are synthesized, spanning classical image processing, machine learning, and deep learning paradigms. A meta‐analysis of 428 studies establishes a unified taxonomy and highlights performance trends, generalization capabilities, and clinical relevance.
Avinash Bansal +6 more
wiley +1 more source
Remote Sensing, 2020 Building extraction is a binary classification task that separates the building area from the background in remote sensing images. The conditional random field (CRF) is directly modelled by the maximum posterior probability, which can make full use of ...
Qiqi Zhu +3 more
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Remote Sensing, 2021 Hyperspectral imagery has been widely used in precision agriculture due to its rich spectral characteristics. With the rapid development of remote sensing technology, the airborne hyperspectral imagery shows detailed spatial information and temporal ...
Lifei Wei +7 more
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