Results 81 to 90 of about 2,584,993 (291)

Memory-based named entity recognition [PDF]

proceeding of the 6th conference on Natural language learning - COLING-02, 2002
We apply a memory-based learner to the CoNLL-2002 shared task: language-independent named entity recognition. We use three additional techniques for improving the base performance of the learner: cascading, feature selection and system combination.
openaire   +4 more sources

Corpora and evaluation tools for multilingual named entity grammar development [PDF]

, 2011
We present an effort for the development of multilingual named entity grammars in a unification-based finite-state formalism (SProUT). Following an extended version of the MUC7 standard, we have developed Named Entity Recognition grammars for German ...
Guasch, Clara, Lehmann, Sabine, Erbach, Gregor, Piskorski, Jakub, Homola, Petr, Shimada, Atsuko, Li, Hong, Xu, Feiyu, Schäfer, Ulrich, Siegel, Melanie, Droźdźyński, Witold, Ziegler-Eisele, Dorothee, Bering, Christian, Krieger, Hans-Ulrich   +13 more
core  

The Price of Precision: A Critical Review of Molecular Diagnostics in Glioma, From Guidelines to Global Disparities

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Gliomas have undergone a profound redefinition over the past decade, transitioning from morphology‐based entities to biologically coherent diseases defined by molecular alterations. The 2021 WHO Classification of Tumors of the Central Nervous System and its 2022 update formalize this shift, establishing integrated diagnosis as the global ...
Maria Guarnaccia, Sebastiano Cavallaro
wiley   +1 more source

Handling named entities and compound verbs in phrase-based statistical machine translation [PDF]

, 2010
Data preprocessing plays a crucial role in phrase-based statistical machine translation (PB-SMT). In this paper, we show how single-tokenization of two types of multi-word expressions (MWE), namely named entities (NE) and compound verbs, as well as ...
Way, Andy, Pal, Santanu, Bandyopadhyay, Sivaji, Kumar Naskar, Sudip, Pecina, Pavel   +4 more
core   +3 more sources

Unsupervised Relation Extraction for E-Learning Applications [PDF]

, 2011
A thesis submitted in partial fulfilment of the requirements of the University of Wolverhampton for the degree of Doctor of PhilosophyIn this modern era many educational institutes and business organisations are adopting the e-Learning approach as it ...
Naveed Afzal, Afzal, Naveed
core   +2 more sources

Named Entity Recognition Based on Multi-scale Attention [PDF]

Jisuanji kexue yu tansuo
The accuracy of named entity recognition (NER) task will promote the research of multiple downstream tasks in natural language field. Due to a large number of nested semantics in text, named entities are recognized difficultly.
TANG Ruixue, QIN Yongbin, CHEN Yanping
doaj   +1 more source

What Do Large Language Models Know About Materials?

Advanced Engineering Materials, EarlyView.
If large language models (LLMs) are to be used inside the material discovery and engineering process, they must be benchmarked for the accurateness of intrinsic material knowledge. The current work introduces 1) a reasoning process through the processing–structure–property–performance chain and 2) a tool for benchmarking knowledge of LLMs concerning ...
Adrian Ehrenhofer, Thomas Wallmersperger, Gianaurelio Cuniberti   +2 more
wiley   +1 more source

Proactive Learning for Named Entity Recognition [PDF]

BioNLP 2017, 2017
The goal of active learning is to minimise the cost of producing an annotated dataset, in which annotators are assumed to be perfect, i.e., they always choose the correct labels. However, in practice, annotators are not infallible, and they are likely to assign incorrect labels to some instances.
Li, Maolin, Nguyen, Nhung, Ananiadou, Sophia; id_orcid 0000-0002-4097-9191   +2 more
openaire   +3 more sources

NFDI MatWerk Ontology (MWO): A BFO‐Compliant Ontology for Research Data Management in Materials Science and Engineering

Advanced Engineering Materials, EarlyView.
This article presents the NFDI‐MatWerk Ontology (MWO), a Basic Formal Ontology‐based framework for interoperable research data management in materials science and engineering (MSE). Covering consortium structures, research data management resources, services, and instruments, MWO enables semantic integration, Findable, Accessible, Interoperable, and ...
Hossein Beygi Nasrabadi, Ebrahim Norouzi, Kostiantyn Hubaiev, Jörg Waitelonis, Harald Sack   +4 more
wiley   +1 more source

Field Report from Collaborative Research Center 1625: Heterogeneous Research Data Management Using Ontology Representations

Advanced Engineering Materials, EarlyView.
A unified research data management framework for heterogeneous materials data is presented. The system integrates multimodal datasets using ontologies and knowledge graphs, enabling interoperability and FAIR (findable, accessible, interoperable, reusable) data principles. By linking data across scales and workflows, it supports reproducible, Artifitial
Doaa Mohamed, Samuel García Vázquez, Behnam Mardani, Victor Dudarev, Alfred Ludwig, Maribel Acosta, Markus Stricker   +6 more
wiley   +1 more source