Results 161 to 170 of about 328,579 (213)

Secondary Operation Risk Assessment Method Integrating Graph Convolutional Networks and Semantic Embeddings. [PDF]

Sensors (Basel)
Zhu P, Li Y, Xu P, Li P, Zhao Z, Li G.
europepmc   +1 more source

Building a credibility-based framework for target discovery: Perspectives from tRNA synthetase-linked metabolic diseases. [PDF]

Brief Bioinform
Choi J, Yoon I, Jung Y, Han S, Kang E, Ahn T, Kim S.   +6 more
europepmc   +1 more source

CEAF: Capsule network enhanced feature fusion architecture for Chinese Named Entity Recognition. [PDF]

PLoS One
Ma S, Liu G, Xu Y.
europepmc   +1 more source

A knowledge graph dataset for broiler farming automatically constructed based on a large language model. [PDF]

Data Brief
Ma N, Kong F, Liu J, Zhang C, Zhao C, Cao S, Sun W.   +6 more
europepmc   +1 more source

CHNRI-B: integrating bibliometric, patent and public-funding intelligence into health research priority setting - a methodological framework for capacity-sensitive priority setting

Radwan A, El-Sakka A, Kassem AM, Kassas ME, Zakhary NI, Elfeky G, Gabr A, Mokhtar A, Barsoum R, Khaled H, Anwar W, Esmat G.   +11 more
europepmc   +1 more source

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Graph neural entity disambiguation

Knowledge-Based Systems, 2020
Abstract Entity Disambiguation (ED) aims to automatically resolve mentions of entities in a document to corresponding entries in a given knowledge base. State-of-the-art ED methods typically utilize local contextual information for obtaining mention embeddings which will be compared to candidate entity embeddings and then apply Conditional Random ...
Chuan Shi, Chao Shao
exaly   +2 more sources

Named Entity Disambiguation at Scale [PDF]

Lecture Notes in Computer Science, 2020
Named Entity Disambiguation (NED) is a crucial task in many Natural Language Processing applications such as entity linking, record linkage, knowledge base construction, or relation extraction, to name a few. The task in NED is to map textual variations of a named entity to its formal name.
Ahmad Aghaebrahimian, Mark Cieliebak
exaly   +3 more sources

Entity disambiguation with memory network

Neurocomputing, 2018
Abstract We develop a computational approach based on memory network for entity disambiguation. The approach automatically finds important clues of a mention from surrounding contexts with attention mechanism, and leverages these clues to facilitate entity disambiguation.
Zhenzhou Ji, Duyu Tang
exaly   +3 more sources

A graph based named entity disambiguation using clique partitioning and semantic relatedness

Data and Knowledge Engineering
Disambiguating name mentions in texts is a crucial task in Natural Language Processing, especially in entity linking. The credibility and efficiency of such systems depend largely on this task.
Farid Meziane
exaly   +2 more sources

System for collective entity disambiguation

Proceedings of the first international workshop on Entity recognition & disambiguation - ERD '14, 2014
We present an approach and a system for collective disambiguation of entity mentions occurring in natural language text. Given an input text, the system spots mentions and their candidate entities. Candidate entities across all mentions are jointly modeled as binary nodes in a Markov Random Field.
Ashish Kulkarni, Kanika Agarwal, Pararth Shah, Sunny Raj Rathod, Ganesh Ramakrishnan   +4 more
openaire   +2 more sources