| 1 |
Between History and Natural Language Processing: Study, Enrichment and Online Publication of French Parliamentary Debates of the Early Third Republic (1881-1899)
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In: ParlaCLARIN III at LREC2022 - Workshop on Creating, Enriching and Using Parliamentary Corpora ; https://hal.archives-ouvertes.fr/hal-03623351 ; ParlaCLARIN III at LREC2022 - Workshop on Creating, Enriching and Using Parliamentary Corpora, Jun 2022, Marseille, France ; https://www.clarin.eu/ParlaCLARIN-III (2022)
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| 2 |
Chinese-Uyghur Bilingual Lexicon Extraction Based on Weak Supervision
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In: Information; Volume 13; Issue 4; Pages: 175 (2022)
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| 3 |
Investigating the Efficient Use of Word Embedding with Neural-Topic Models for Interpretable Topics from Short Texts
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In: Sensors; Volume 22; Issue 3; Pages: 852 (2022)
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| 4 |
Analysis of the Effects of Lockdown on Staff and Students at Universities in Spain and Colombia Using Natural Language Processing Techniques
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In: International Journal of Environmental Research and Public Health; Volume 19; Issue 9; Pages: 5705 (2022)
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| 5 |
An Enhanced Neural Word Embedding Model for Transfer Learning
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In: Applied Sciences; Volume 12; Issue 6; Pages: 2848 (2022)
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| 6 |
Deep Sentiment Analysis Using CNN-LSTM Architecture of English and Roman Urdu Text Shared in Social Media
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In: Applied Sciences; Volume 12; Issue 5; Pages: 2694 (2022)
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| 7 |
Predicting Academic Performance: Analysis of Students’ Mental Health Condition from Social Media Interactions
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In: Behavioral Sciences; Volume 12; Issue 4; Pages: 87 (2022)
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| 8 |
Vec2Dynamics: A Temporal Word Embedding Approach to Exploring the Dynamics of Scientific Keywords—Machine Learning as a Case Study
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In: Big Data and Cognitive Computing; Volume 6; Issue 1; Pages: 21 (2022)
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| 9 |
Methods, Models and Tools for Improving the Quality of Textual Annotations
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In: Modelling; Volume 3; Issue 2; Pages: 224-242 (2022)
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| 10 |
Creating multi-scripts sentiment analysis lexicons for Algerian, Moroccan and Tunisian dialects
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In: 7th International Conference on Data Mining (DTMN 2021) Computer Science Conference Proceedings in Computer Science & Information Technology (CS & IT) ; https://hal.archives-ouvertes.fr/hal-03308111 ; 7th International Conference on Data Mining (DTMN 2021) Computer Science Conference Proceedings in Computer Science & Information Technology (CS & IT), Sep 2021, Copenhagen, Denmark (2021)
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| 11 |
Bilingual English-German word embedding models for scientific text ...
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| 12 |
Bilingual English-German word embedding models for scientific text ...
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| 13 |
以《Cofacts 真的假的》資料庫為基礎建立中文科學假訊息之探勘模型 ; Text Mining Model for Detecting Chinese Fake Scientific Messages based on Cofacts Open Data
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| 14 |
Automatic Part-of-Speech Tagging for Security Vulnerability Descriptions ...
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| 15 |
Automatic Part-of-Speech Tagging for Security Vulnerability Descriptions ...
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| 17 |
WELFake dataset for fake news detection in text data ...
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Abstract:
We designed a larger and more generic Word Embedding over Linguistic Features for Fake News Detection (WELFake) dataset of 72,134 news articles with 35,028 real and 37,106 fake news. For this, we merged four popular news datasets (i.e. Kaggle, McIntire, Reuters, BuzzFeed Political) to prevent over-fitting of classifiers and to provide more text data for better ML training. Dataset contains four columns: Serial number (starting from 0); Title (about the text news heading); Text (about the news content); and Label (0 = fake and 1 = real). There are 78098 data entries in csv file out of which only 72134 entries are accessed as per the data frame. This dataset is a part of our ongoing research on "Fake News Prediction on Social Media Website" as a doctoral degree program of Mr. Pawan Kumar Verma and is partially supported by the ARTICONF project funded by the European Union’s Horizon 2020 research and innovation program. ... : {"references": ["Benjamin political news dataset, https://github.com/rpitrust/fakenewsdata1, Accessed: 31 March 2020.", "Burfoot satire news dataset, http://www.csse.unimelb.edu.au/research/lt/ resources/satire, Accessed: 31 March 2020.", "Buzzfeed news dataset, https://github.com/BuzzFeedNews/2016-10-facebook-fact-check/tree/master/data, Accessed: 31 March 2020.", "Credbank dataset, http://compsocial.github.io/CREDBANK-data, Accessed: 31 March 2020.", "Fake news challenge dataset, https://github.com/FakeNewsChallenge/fnc-1, Accessed: 31 March 2020.", "Fakenewsnet dataset, https://github.com/KaiDMML/FakeNewsNet, Accessed: 31 March 2020.", "Liar dataset, https://www.cs.ucsb.edu/~william/data/liar\_dataset.zip, Accessed:31 March 2020.", "Verma P.K., Agrawal P. (2020). Study and Detection of Fake News: P2C2-Based Machine Learning Approach, International Conference on Data Management, Analytics and Innovation, pp. 261-278, Delhi. https://doi.org/10.1007/978-981-15-5619-7_18", "P. K. Verma, P. Agrawal, I. Amorim ...
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Keyword:
fake news detection; linguistic features; machine learning; text classification; Text news; word embedding
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URL: https://dx.doi.org/10.5281/zenodo.4561253
https://zenodo.org/record/4561253
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| 18 |
Text ranking based on semantic meaning of sentences ; Textrankning baserad på semantisk betydelse hos meningar
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| 19 |
Efficient Estimate of Low-Frequency Words’ Embeddings Based on the Dictionary: A Case Study on Chinese
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In: Applied Sciences ; Volume 11 ; Issue 22 (2021)
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| 20 |
Acoustic Word Embeddings for End-to-End Speech Synthesis
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In: Applied Sciences ; Volume 11 ; Issue 19 (2021)
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