Results 51 to 60 of about 321,541 (313)
CRFVoter: gene and protein related object recognition using a conglomerate of CRF-based tools
Journal of Cheminformatics, 2019 Background Gene and protein related objects are an important class of entities in biomedical research, whose identification and extraction from scientific articles is attracting increasing interest.
Wahed Hemati, Alexander Mehler
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 +4 more
core +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 +5 more
core +1 more source
Retractions in rheumatology: trends, causes, and implications for research integrity
Arthritis Care &Research, Accepted Article.Objective We aimed to describe the trends and main reasons for study retraction in rheumatology literature. Methods We reviewed the Retraction Watch database to identify retracted articles in rheumatology. We recorded the main study characteristics, authors’ countries, reasons for retraction, time from publication to retraction, and trends over time ...
Anna Maria Vettori, Michele Iudici
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 +4 more
doaj +1 more source
Named Entity Recognition in Twitter using Images and Text
, 2017 Named Entity Recognition (NER) is an important subtask of information extraction that seeks to locate and recognise named entities. Despite recent achievements, we still face limitations with correctly detecting and classifying entities, prominently in ...
Esteves, Diego +3 more
core +1 more source
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 +3 more
wiley +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 +2 more
wiley +1 more source
Challenges and solutions for Latin named entity recognition [PDF]
, 2016 Although spanning thousands of years and genres as diverse as liturgy, historiography, lyric and other forms of prose and poetry, the body of Latin texts is still relatively sparse compared to English.
Ajaka, Petra +6 more
core
Improving Multilingual Named Entity Recognition with Wikipedia Entity Type Mapping
, 2016 The state-of-the-art named entity recognition (NER) systems are statistical machine learning models that have strong generalization capability (i.e., can recognize unseen entities that do not appear in training data) based on lexical and contextual ...
Florian, Radu, Ni, Jian
core +1 more source