Results 151 to 160 of about 14,087 (197)

Combining large language models with enterprise knowledge graphs: a perspective on enhanced natural language understanding. [PDF]

Front Artif Intell
Mariotti L, Guidetti V, Mandreoli F, Belli A, Lombardi P.   +4 more
europepmc   +1 more source

Medical named entity recognition based on domain knowledge and position encoding. [PDF]

BMC Med Inform Decis Mak
Sun S, Hu Q, Xu F, Hu F, Wu Y, Wang B.
europepmc   +1 more source

Synergizing a knowledge graph and large language model for relay catalysis pathway recommendation. [PDF]

Natl Sci Rev
Fu F, Li QQ, Wang F, Hu J, Wang TT, Liu YP, Xu W, Lin Z, Gong FQ, Fan QY, Pan JZ, Wang Y, Cheng J.   +12 more
europepmc   +1 more source

Clinical insights: A comprehensive review of language models in medicine. [PDF]

PLOS Digit Health
Neveditsin N, Lingras P, Mago V.
europepmc   +1 more source

An Empirical Evaluation of Prompting Strategies for Large Language Models in Zero-Shot Clinical Natural Language Processing: Algorithm Development and Validation Study.

JMIR Med Inform
Sivarajkumar S, Kelley M, Samolyk-Mazzanti A, Visweswaran S, Wang Y.   +4 more
europepmc   +1 more source

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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   +3 more sources

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   +1 more source

Named Entity Disambiguation Using HMMs

2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT), 2013
In this paper we present a novel approach to disambiguate textual mentions of named entities against the Wikipedia knowledge base. The conditional dependencies between different named entities across Wikipedia are represented as a Markov network. In our approach, named entities are treated as hidden variables and textual mentions as observations.
Ayman Alhelbawy, Robert Gaizauskas
openaire   +1 more source