| 1 |
MetaboListem and TABoLiSTM: Two Deep Learning Algorithms for Metabolite Named Entity Recognition
|
|
|
|
In: Metabolites; Volume 12; Issue 4; Pages: 276 (2022)
|
|
BASE
|
|
Show details
|
|
|
| 2 |
An Explainable Fake News Detector Based on Named Entity Recognition and Stance Classification Applied to COVID-19
|
|
|
|
In: Information; Volume 13; Issue 3; Pages: 137 (2022)
|
|
BASE
|
|
Show details
|
|
|
| 3 |
StaResGRU-CNN with CMedLMs: a stacked residual GRU-CNN with pre-trained biomedical language models for predictive intelligence
|
|
|
|
BASE
|
|
Show details
|
|
|
| 4 |
Medieval manuscripts from digitization to historical analysis
|
|
|
|
In: On the way to the future of Digital Manuscript Studies ; https://hal.archives-ouvertes.fr/hal-03503308 ; On the way to the future of Digital Manuscript Studies, Radboud University, Oct 2021, Nijmegen, Netherlands ; https://www.ru.nl/rich/news-events/events/redactionele/online-workshop-on-the-way-to-the-future-digital/ (2021)
|
|
BASE
|
|
Show details
|
|
|
| 5 |
Named Entity Recognition for French medieval charters
|
|
|
|
In: Workshop on Natural Language Processing for Digital Humanities ; https://hal.archives-ouvertes.fr/hal-03503055 ; Workshop on Natural Language Processing for Digital Humanities, Dec 2021, Helsinki, Finland (2021)
|
|
BASE
|
|
Show details
|
|
|
| 6 |
Named-Entity Dataset for Medieval Latin, Middle High German and Old Norse
|
|
|
|
In: Journal of Open Humanities Data; Vol 7 (2021); 23 ; 2059-481X (2021)
|
|
BASE
|
|
Show details
|
|
|
| 7 |
A Survey on Recent Named Entity Recognition and Relationship Extraction Techniques on Clinical Texts
|
|
|
|
In: Applied Sciences ; Volume 11 ; Issue 18 (2021)
|
|
BASE
|
|
Show details
|
|
|
| 8 |
HTLinker: A Head-to-Tail Linker for Nested Named Entity Recognition
|
|
|
|
In: Symmetry ; Volume 13 ; Issue 9 (2021)
|
|
BASE
|
|
Show details
|
|
|
| 9 |
Optimizing Small BERTs Trained for German NER
|
|
|
|
In: Information ; Volume 12 ; Issue 11 (2021)
|
|
BASE
|
|
Show details
|
|
|
| 10 |
Entity-aware capsule network for multi-class classification of big data: a deep learning approach
|
|
|
|
BASE
|
|
Show details
|
|
|
| 11 |
Normalisation of 16th and 17th century texts in French and geographical named entity recognition
|
|
|
|
In: 4th ACM SIGSPATIAL International Workshop on Geospatial Humanities ; ACM SIGSPATIAL GeoHumanities'20 ; https://hal-upec-upem.archives-ouvertes.fr/hal-02955867 ; ACM SIGSPATIAL GeoHumanities'20, ACM, Nov 2020, Seattle (virtual), United States. pp.28-34, ⟨10.1145/3423337.3429437⟩ ; https://ludovicmoncla.github.io/sigspatial-geohumanities-2020/ (2020)
|
|
BASE
|
|
Show details
|
|
|
| 12 |
Named Entity Recognition for Sensitive Data Discovery in Portuguese
|
|
|
|
In: Applied Sciences ; Volume 10 ; Issue 7 (2020)
|
|
BASE
|
|
Show details
|
|
|
| 13 |
Natural Language Processing Model for Automatic Analysis of Cybersecurity-Related Documents
|
|
|
|
In: Symmetry ; Volume 12 ; Issue 3 (2020)
|
|
BASE
|
|
Show details
|
|
|
| 14 |
Learning Subword Embedding to Improve Uyghur Named-Entity Recognition
|
|
|
|
In: Information ; Volume 10 ; Issue 4 (2019)
|
|
BASE
|
|
Show details
|
|
|
| 15 |
Bidirectional Recurrent Neural Network Approach for Arabic Named Entity Recognition
|
|
|
|
In: Future Internet ; Volume 10 ; Issue 12 (2018)
|
|
BASE
|
|
Show details
|
|
|
| 16 |
E-Petition Popularity: Do Linguistic and Semantic Factors Matter?
|
|
|
|
In: School of Information Faculty Publications (2016)
|
|
BASE
|
|
Show details
|
|
|
| 17 |
Introducing Textual Analysis Tools for Policy Informatics: A Case Study of E-petitions
|
|
|
|
In: School of Information Faculty Publications (2015)
|
|
BASE
|
|
Show details
|
|
|
| 18 |
Water Relationships in the U.S. Southwest: Characterizing Water Management Networks Using Natural Language Processing
|
|
|
|
In: Water; Volume 6; Issue 6; Pages: 1601-1641 (2014)
|
|
BASE
|
|
Show details
|
|
|
| 19 |
Water Relationships in the U.S. Southwest: Characterizing Water Management Networks Using Natural Language Processing
|
|
|
|
In: Faculty Publications (2014)
|
|
BASE
|
|
Show details
|
|
|
| 20 |
Using Empirically Constructed Lexical Resources for Named Entity Recognition. Biomed Inform Insights
|
|
|
|
In: http://www.la-press.com/redirect_file.php?fileId%3D5041%26fileType%3Dpdf%26filename%3D3738-BII-Using-Empirically-Constructed-Lexical-Resources-for-Named-Entity-Recog.pdf (2013)
|
|
BASE
|
|
Show details
|
|
|
|
|
