Results 51 to 60 of about 318,360 (330)

HMM based Korean Named Entity Recognition [PDF]

Journal of Systemics, Cybernetics and Informatics, 2003
In this paper, we present a named entity recognition model for Korean Language. Named entity recognition is an essential and important process of Question Answering and Information Extraction system.
Yi-Gyu Hwang, Eui-Sok Chung, Soo-jong Lim   +2 more
doaj  

Deep learning-based methods for natural hazard named entity recognition

Scientific Reports, 2022
Natural hazard named entity recognition is a technique used to recognize natural hazard entities from a large number of texts. The method of natural hazard named entity recognition can facilitate acquisition of natural hazards information and provide ...
Junlin Sun, Yanrong Liu, Jing Cui, Handong He   +3 more
doaj   +1 more source

ChineseCTRE: A Model for Geographical Named Entity Recognition and Correction Based on Deep Neural Networks and the BERT Model

ISPRS International Journal of Geo-Information, 2023
Social media is widely used to share real-time information and report accidents during natural disasters. Named entity recognition (NER) is a fundamental task of geospatial information applications that aims to extract location names from natural ...
Wei Zhang, Jingtao Meng, Jianhua Wan, Chengkun Zhang, Jiajun Zhang, Yuanyuan Wang, Liuchang Xu, Fei Li   +7 more
doaj   +1 more source

What Is the Role of Giant Endosomal Sorting Complexes Required for Transport (ESCRT) Structures in T Cell Activation?

Advanced Biology, EarlyView.
The article explores the discovery of unusually large, ring‐shaped ESCRT protein structures formed by immune and stromal cells. It investigates their formation, composition, and potential roles in immune synapse formation. The findings challenge assumptions about their function in immunological synapses, suggesting an exclusive role in maintaining ...
Anthi Psoma, Femmy C. Stempels, Rinse de Boer, Geert van den Bogaart   +3 more
wiley   +1 more source

Few-shot classification in Named Entity Recognition Task

, 2018
For many natural language processing (NLP) tasks the amount of annotated data is limited. This urges a need to apply semi-supervised learning techniques, such as transfer learning or meta-learning.
Akhundov Adnan, Cotterell Ryan, Ma Yukun, Pradhan Sameer, Ravi Sachin, Snell Jake   +5 more
core   +1 more source

Enabling Digital Continuity in Virtual Manufacturing for Eco‐Efficiency Assessment of Lightweight Structures by Means of a Domain‐Specific Structural Mechanics Language: Requirements, Idea and Proof of Concept

Advanced Engineering Materials, EarlyView.
This article presents a solver‐agnostic domain‐specific language (DSL) for computational structural mechanics that strengthens interoperability in virtual product development. Using a hierarchical data model, the DSL enables seamless exchange between diverse simulation tools and numerical methods.
Martin Rädel, Andreas Schuster, Felix Schoenitz, Jean Lefèvre   +3 more
wiley   +1 more source

Local Feature Enhancement for Nested Entity Recognition Using a Convolutional Block Attention Module

Applied Sciences, 2023
Named entity recognition involves two main types: nested named entity recognition and flat named entity recognition. The span-based approach treats nested entities and flat entities uniformly by classifying entities on a span representation. However, the
Jinxin Deng, Junbao Liu, Xiaoqin Ma, Xizhong Qin, Zhenhong Jia   +4 more
doaj   +1 more source

Deep Active Learning for Named Entity Recognition [PDF]

, 2018
Deep learning has yielded state-of-the-art performance on many natural language processing tasks including named entity recognition (NER). However, this typically requires large amounts of labeled data.
Anandkumar, Animashree, Kronrod, Yakov, Lipton, Zachary C., Shen, Yanyao, Yun, Hyokun   +4 more
core   +1 more source

Dynamic Named Entity Recognition

Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing, 2023
8 pages, 6 figures, SAC ...
Luiggi, Tristan, Guigue, Vincent, Soulier, Laure, Jendoubi, Siwar, Baelde, Aurelien   +4 more
openaire   +3 more sources

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