| 1 |
Linked Open Tafsir - Rekonstruktion der Entstehungsdynamik(en) des Korans mithilfe der Netzwerkmodellierung früher islamischer Überlieferungen ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 2 |
Linked Open Tafsir - Rekonstruktion der Entstehungsdynamik(en) des Korans mithilfe der Netzwerkmodellierung früher islamischer Überlieferungen ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 3 |
EMBEDDIA tools output example corpus of Estonian, Croatian and Latvian news articles 1.0
|
|
|
|
BASE
|
|
Show details
|
|
|
| 8 |
Measuring Semantic Similarity of Documents by Using Named Entity Recognition Methods
|
|
|
|
In: Masters (2022)
|
|
BASE
|
|
Show details
|
|
|
| 9 |
An RG-FLAT-CRF Model for Named Entity Recognition of Chinese Electronic Clinical Records
|
|
|
|
In: Electronics; Volume 11; Issue 8; Pages: 1282 (2022)
|
|
BASE
|
|
Show details
|
|
|
| 10 |
Analysis of the Full-Size Russian Corpus of Internet Drug Reviews with Complex NER Labeling Using Deep Learning Neural Networks and Language Models
|
|
|
|
In: Applied Sciences; Volume 12; Issue 1; Pages: 491 (2022)
|
|
BASE
|
|
Show details
|
|
|
| 11 |
S-NER: A Concise and Efficient Span-Based Model for Named Entity Recognition
|
|
|
|
In: Sensors; Volume 22; Issue 8; Pages: 2852 (2022)
|
|
BASE
|
|
Show details
|
|
|
| 12 |
A Pipeline Approach to Context-Aware Handwritten Text Recognition
|
|
|
|
In: Applied Sciences; Volume 12; Issue 4; Pages: 1870 (2022)
|
|
BASE
|
|
Show details
|
|
|
| 13 |
Research on Named Entity Recognition Methods in Chinese Forest Disease Texts
|
|
|
|
In: Applied Sciences; Volume 12; Issue 8; Pages: 3885 (2022)
|
|
BASE
|
|
Show details
|
|
|
| 14 |
Learning the Morphological and Syntactic Grammars for Named Entity Recognition
|
|
|
|
In: Information; Volume 13; Issue 2; Pages: 49 (2022)
|
|
BASE
|
|
Show details
|
|
|
| 15 |
Comparison of Text Mining Models for Food and Dietary Constituent Named-Entity Recognition
|
|
|
|
In: Machine Learning and Knowledge Extraction; Volume 4; Issue 1; Pages: 254-275 (2022)
|
|
BASE
|
|
Show details
|
|
|
| 16 |
A Novel Method of Generating Geospatial Intelligence from Social Media Posts of Political Leaders
|
|
|
|
In: Information; Volume 13; Issue 3; Pages: 120 (2022)
|
|
Abstract:
Social media platforms such as Twitter have been used by political leaders, heads of states, political parties, and their supporters to strategically influence public opinions. Leaders can post about a location, a state, a country, or even a region in their social media accounts, and the posts can immediately be viewed and reacted to by millions of their followers. The effect of social media posts by political leaders could be automatically measured by extracting, analyzing, and producing real-time geospatial intelligence for social scientists and researchers. This paper proposed a novel approach in automatically processing real-time social media messages of political leaders with artificial intelligence (AI)-based language detection, translation, sentiment analysis, and named entity recognition (NER). This method automatically generates geospatial and location intelligence on both ESRI ArcGIS Maps and Microsoft Bing Maps. The proposed system was deployed from 1 January 2020 to 6 February 2022 to analyze 1.5 million tweets. During this 25-month period, 95K locations were successfully identified and mapped using data of 271,885 Twitter handles. With an overall 90% precision, recall, and F1score, along with 97% accuracy, the proposed system reports the most accurate system to produce geospatial intelligence directly from live Twitter feeds of political leaders with AI.
|
|
Keyword:
analyzing tweets of political leaders; big data processing of social media; geospatial intelligence; named entity recognition on political tweets; sentiment analysis on political tweets
|
|
URL: https://doi.org/10.3390/info13030120
|
|
BASE
|
|
Hide details
|
|
|
| 17 |
MetaboListem and TABoLiSTM: Two Deep Learning Algorithms for Metabolite Named Entity Recognition
|
|
|
|
In: Metabolites; Volume 12; Issue 4; Pages: 276 (2022)
|
|
BASE
|
|
Show details
|
|
|
| 18 |
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
|
|
|
| 19 |
StaResGRU-CNN with CMedLMs: a stacked residual GRU-CNN with pre-trained biomedical language models for predictive intelligence
|
|
|
|
BASE
|
|
Show details
|
|
|
| 20 |
Effect of depth order on iterative nested named entity recognition models
|
|
|
|
In: Conference on Artificial Intelligence in Medecine (AIME 2021) ; https://hal.archives-ouvertes.fr/hal-03277643 ; Conference on Artificial Intelligence in Medecine (AIME 2021), Jun 2021, Porto, Portugal (2021)
|
|
BASE
|
|
Show details
|
|
|
|
|
