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SemEval-2023 Task 2: Fine-grained Multilingual Named Entity Recognition (MultiCoNER 2) [PDF]
International Workshop on Semantic Evaluation, 2023 We present the findings of SemEval-2023 Task 2 on Fine-grained Multilingual Named Entity Recognition (MultiCoNER 2). Divided into 13 tracks, the task focused on methods to identify complex fine-grained named entities (like WRITTENWORK, VEHICLE ...
B. Fetahu +4 more
semanticscholar +1 more source
Review of Chinese Named Entity Recognition Research [PDF]
Jisuanji kexue yu tansuo, 2023 With the rapid development of related technologies in the field of natural language processing, as an upstream task of natural language processing, improving the accuracy of named entity recognition is of great significance for subsequent text processing
WANG Yingjie, ZHANG Chengye, BAI Fengbo, WANG Zumin, JI Changqing
doaj +1 more source
DiffusionNER: Boundary Diffusion for Named Entity Recognition [PDF]
Annual Meeting of the Association for Computational Linguistics, 2023 In this paper, we propose DiffusionNER, which formulates the named entity recognition task as a boundary-denoising diffusion process and thus generates named entities from noisy spans.
Yongliang Shen +5 more
semanticscholar +1 more source
MULTITHREAD IN NAMED ENTITY RECOGNITION [PDF]
International Journal of Intelligent Computing and Information Sciences, 2016 According to Gordon Earle Moore, Every two years, the number of core on a CPU chip is doubling. So we change our program to use threads for different reasons, program will run faster and make better use of the multiple CPU/core architecture that you are ...
Z. Rabea, M. Abu Elsoud, M. Rashed
doaj +1 more source
A Survey on Deep Learning for Named Entity Recognition [PDF]
IEEE Transactions on Knowledge and Data Engineering, 2018 Named entity recognition (NER) is the task to identify mentions of rigid designators from text belonging to predefined semantic types such as person, location, organization etc.
J. Li +3 more
semanticscholar +1 more source
Transformer-Based Named Entity Recognition in Construction Supply Chain Risk Management in Australia [PDF]
IEEE Access, 2023 In the Australian construction industry, effective supply chain risk management (SCRM) is critical due to its complex networks and susceptibility to various risks.
Milad Baghalzadeh Shishehgarkhaneh +4 more
semanticscholar +1 more source
Does semantics aid syntax? An empirical study on named entity recognition and classification [PDF]
, 2022 Many researchers jointly model multiple linguistic tasks (e.g., joint modeling of named entity recognition and named entity classification and joint modeling of syntactic parsing and semantic parsing) with an implicit assumption that these individual ...
Hussain, Amir +2 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 +4 more
openaire +4 more sources
BERN2: an advanced neural biomedical named entity recognition and normalization tool [PDF]
Bioinform., 2022 In biomedical natural language processing, named entity recognition (NER) and named entity normalization (NEN) are key tasks that enable the automatic extraction of biomedical entities (e.g. diseases and drugs) from the ever-growing biomedical literature.
Mujeen Sung +5 more
semanticscholar +1 more source
A Unified MRC Framework for Named Entity Recognition [PDF]
Annual Meeting of the Association for Computational Linguistics, 2019 The task of named entity recognition (NER) is normally divided into nested NER and flat NER depending on whether named entities are nested or not.Models are usually separately developed for the two tasks, since sequence labeling models, the most widely ...
Xiaoya Li +5 more
semanticscholar +1 more source