Results 91 to 100 of about 17,118 (185)

OnD-CRF: predicting order and disorder in proteins using [corrected] conditional random fields.

Bioinformatics (Oxford, England), 2008
Order and Disorder prediction using Conditional Random Fields (OnD-CRF) is a new method for accurately predicting the transition between structured and mobile or disordered regions in proteins. OnD-CRF applies CRFs relying on features which are generated from the amino acids sequence and from secondary structure prediction.
Lixiao, Wang, Uwe H, Sauer
openaire   +1 more source

Entity recognition in Chinese clinical text using attention-based CNN-LSTM-CRF

BMC Medical Informatics and Decision Making, 2019
Background Clinical entity recognition as a fundamental task of clinical text processing has been attracted a great deal of attention during the last decade. However, most studies focus on clinical text in English rather than other languages. Recently, a
Buzhou Tang, Xiaolong Wang, Jun Yan, Qingcai Chen   +3 more
doaj   +1 more source

KnowNER: Incremental Multilingual Knowledge in Named Entity Recognition

, 2017
KnowNER is a multilingual Named Entity Recognition (NER) system that leverages different degrees of external knowledge. A novel modular framework divides the knowledge into four categories according to the depth of knowledge they convey.
Del Corro, Luciano, Dembelova, Tatiana, Hoffart, Johannes, Seyler, Dominic, Weikum, Gerhard   +4 more
core  

Evaluation of spatial variability characteristics based on anisotropic modes of random fields

Journal of Rock Mechanics and Geotechnical Engineering
This paper introduces a framework for modeling random fields, with a particular emphasis on analyzing anisotropic spatial variability. It establishes a clear connection between the correlation function and the Kriging variogram across various anisotropic
Kejing Chen, Qinghui Jiang
doaj   +1 more source

SL-CRF: A Framework for Symbolic Logic Integration in Conditional Random Fields

Abstract: This paper presents the mathematical foundation of Symbolic Logic - Conditional Random Fields (SL-CRF), a novel framework designed to integrate rigid axiomatic constraints into probabilistic graphical models. While modern Large Language Models (LLMs) excel at probabilistic pattern matching, they fundamentally lack the ability to maintain ...
openaire   +2 more sources

Fully‐connected semantic segmentation of hyperspectral and LiDAR data

IET Computer Vision, 2019
Semantic segmentation is an emerging field in the computer vision community where one can segment and label an object all at once, by considering the effects of the neighbouring pixels. In this study, the authors propose a new semantic segmentation model
Hakan Aytaylan, Seniha Esen Yuksel
doaj   +1 more source

Improving SpaCy-based NER Accuracy using Conditional Random Fields (CRF) as Advanced Features for Javanese Legends

ICoBITS
Named Entity Recognition (NER) is a crucial task for information extraction, particularly for preserving the rich cultural data within Javanese legends. However, standard NER frameworks like SpaCy can face limitations when processing languages with unique linguistic characteristics.
Kevin Dwi Mahendra, Danang Arbian Sulistyo   +1 more
openaire   +1 more source

A Deep-Structured Conditional Random Field Model for Object Silhouette Tracking.

PLoS ONE, 2015
In this work, we introduce a deep-structured conditional random field (DS-CRF) model for the purpose of state-based object silhouette tracking. The proposed DS-CRF model consists of a series of state layers, where each state layer spatially characterizes
Mohammad Javad Shafiee, Zohreh Azimifar, Alexander Wong   +2 more
doaj   +1 more source

Sentiment Analysis Based on Improved Transformer Model and Conditional Random Fields

IEEE Access
With the rapid development of the Internet, people independently write comments with emotional characteristics on e-commerce platforms, which express consumers’ emotional tendencies towards products or services from multiple perspectives.
Lisha Yao, Ni Zheng
doaj   +1 more source

NERP-CRF: uma ferramenta para o reconhecimento de entidades nomeadas por meio de Conditional Random Fields.

Linguamática, 2014
Conditional Random Fields (CRF) é um método probabilístico de predição estruturada que tem sido amplamente aplicado em diversas áreas, tais como a de Processamento da Linguagem Natural (PLN), incluindo o Reconhecimento de Entidades Nomeadas (REN), visão computacional e bioinformática.
Daniela Oliveira F. do Amaral, Renata Vieira   +1 more
openaire   +2 more sources