Results 151 to 160 of about 1,142,269 (203)

Knowledge graph for traditional Chinese medicine diagnosis and treatment of diabetic retinopathy: design, construction, and applications. [PDF]

Int J Ophthalmol
Xiao L, Wang JW, Wang CW, Wang Y, Yan JF, Peng QH.   +5 more
europepmc   +1 more source

Accurate Clinical Entity Recognition and Code Mapping of Anatomopathological Reports Using BioClinicalBERT Enhanced by Retrieval-Augmented Generation: A Hybrid Deep Learning Approach. [PDF]

Bioengineering (Basel)
Abdaoui H, Barki C, Dergaa I, Tlili K, Ceylan Hİ, Bragazzi NL, de Giorgio A, Ben Salah R, Rahmouni HB.   +8 more
europepmc   +1 more source

Bibliometrics beyond citations: introducing mention extraction and analysis. [PDF]

Scientometrics
Petrovich E, Verhaegh S, Bös G, Cristalli C, Dewulf F, van Gemert T, IJdens N.   +6 more
europepmc   +1 more source

Research information systems and knowledge graphs: a review. [PDF]

Front Res Metr Anal
Haris M, Auer S, Stocker M.
europepmc   +1 more source

BELHD: improving biomedical entity linking with homonym disambiguation. [PDF]

Bioinformatics
Garda S, Leser U.
europepmc   +1 more source

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.
Ahmad Aghaebrahimian, Mark Cieliebak
exaly   +5 more sources

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Location-Aware Named Entity Disambiguation

Proceedings of the 30th ACM International Conference on Information & Knowledge Management, 2021
Named Entity Disambiguation (NED) and linking has been traditionally evaluated on natural language content that is both well-written and contextually rich. However, many NED approaches display poor performance on text sources that are short and noisy.
Maithrreye Srinivasan, Davood Rafiei
openaire   +2 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

Learning Entity Representation for Named Entity Disambiguation

2015
In this paper we present a novel disambiguation model, based on neural networks. Most existing studies focus on designing effective man-made features and complicated similarity measures to obtain better disambiguation performance. Instead, our method learns distributed representation of entity to measure similarity without man-made features.
Rui Cai 0002, Houfeng Wang, Junhao Zhang
openaire   +2 more sources

SVM ensembles for named entity disambiguation

Computing, 2019
The enormous quantity of digital data necessitates automation, which among other things can help link unstructured to structured data. Such a task requires a systematic approach of mapping entity mentions (e.g., person, location) to corresponding entries in a Knowledge Base.
Amal Alokaili, Mohamed El Bachir Menai
openaire   +1 more source