Results 31 to 40 of about 314,969 (284)

Arabic named entity recognition. [PDF]

, 2015
En esta tesis doctoral se describen las investigaciones realizadas con el objetivo de determinar las mejores tecnicas para construir un Reconocedor de Entidades Nombradas en Arabe. Tal sistema tendria la habilidad de identificar y clasificar las entidades nombradas que se encuentran en un texto arabe de dominio abierto. La tarea de
openaire   +2 more sources

Named Entity Recognition of Wheat Diseases and Pests Fusing ALBERT and Rules [PDF]

Jisuanji kexue yu tansuo, 2023
Named entity recognition of wheat diseases and pests is a key step to building a knowledge graph. Aiming at the problems of lack of training data, complex entity structure, diverse entity types and uneven entity distribution in wheat diseases and pests ...
LIU Hebing, ZHANG Demeng, XIONG Shufeng, MA Xinming, XI Lei
doaj   +1 more source

Chinese Overlapping Named Entity Recognition Method Based on Label Clustering [PDF]

Jisuanji gongcheng, 2020
To address complex nested relations between named entities and overlapping boundaries of adjacent named entities caused by mislabeling in corpus,this paper proposes a method of Chinese overlapping Named Entity Recognition(NER).First,a hierarchical ...
WEN Xiuxiu, MA Chao, GAO Yuanyuan, KANG Zilu
doaj   +1 more source

Unsupervised Named-Entity Recognition: Generating Gazetteers and Resolving Ambiguity [PDF]

, 2006
In this paper, we propose a named-entity recognition (NER) system that addresses two major limitations frequently discussed in the field. First, the system requires no human intervention such as manually labeling training data or creating gazetteers ...
Matwin, Stan, Nadeau, David, Turney, Peter D.   +2 more
core   +2 more sources

Bipartite Flat-Graph Network for Nested Named Entity Recognition

, 2020
In this paper, we propose a novel bipartite flat-graph network (BiFlaG) for nested named entity recognition (NER), which contains two subgraph modules: a flat NER module for outermost entities and a graph module for all the entities located in inner ...
Luo, Ying, Zhao, Hai
core   +1 more source

Pattern Mining for Named Entity Recognition [PDF]

, 2011
Revised selected paper of the LTC'2011 conferenceInternational audienceMany evaluation campaigns have shown that knowledge-based and data-driven approaches remain equally competitive for Named Entity Recognition.
Antoine, Jean-Yves, Friburger, Nathalie, Nouvel, Damien   +2 more
core   +2 more sources

Pattern Mining for Named Entity Recognition [PDF]

, 2014
International audienceMany evaluation campaigns have shown that knowledge-based and data-driven approaches remain equally competitive for Named Entity Recognition.
B Bouchou, D Nadeau, DD McDonald, F Pedregosa, N Friburger, O Etzioni   +5 more
core   +3 more sources

Neural Architectures for Named Entity Recognition [PDF]

Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2016
State-of-the-art named entity recognition systems rely heavily on hand-crafted features and domain-specific knowledge in order to learn effectively from the small, supervised training corpora that are available. In this paper, we introduce two new neural architectures---one based on bidirectional LSTMs and conditional random fields, and the other that ...
Lample, Guillaume, Ballesteros, Miguel, Subramanian, Sandeep, Kawakami, Kazuya, Dyer, Chris   +4 more
openaire   +3 more sources

Biomedical Flat and Nested Named Entity Recognition: Methods, Challenges, and Advances

Applied Sciences
Biomedical named entity recognition (BioNER) aims to identify and classify biomedical entities (i.e., diseases, chemicals, and genes) from text into predefined classes. This process serves as an important initial step in extracting biomedical information
Yesol Park, Gyujin Son, Mina Rho
doaj   +1 more source

AWdpCNER: Automated Wdp Chinese Named Entity Recognition from Wheat Diseases and Pests Text

Agriculture, 2023
Chinese named entity recognition of wheat diseases and pests is an initial and key step in constructing knowledge graphs. In the field of wheat diseases and pests, there are problems, such as lack of training data, nested entities, fuzzy entity ...
Demeng Zhang, Guang Zheng, Hebing Liu, Xinming Ma, Lei Xi   +4 more
doaj   +1 more source