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Found 8 papers, 0 papers with code
Phraseformer: Multimodal Key-phrase Extraction using Transformer and Graph Embedding
In Phraseformer, each keyword candidate is presented by a vector which is the concatenation of the text and structure learning representations.
Ranked #1 on
Keyword Extraction
on SemEval-2017 Task-10
Cy: Chaotic yolo for user intended image encryption and sharing in social media
Social media is an inseparable part of our daily life where we post and share photos and media related to our life and in some cases we intend to share them between specific people.
A Deep Content-Based Model for Persian Rumor Verification
Therefore, it is important to detect and verify the rumors in the early stage of their spreading.
Ranked #1 on
Rumour Detection
on Sepehr_RumTel01
A semi-supervised model for Persian rumor verification based on content information
It indicates that speech act and WS alongside semantic contextual vectors are helpful features in the rumor verification task.
Ranked #2 on
Rumour Detection
on Sepehr_RumTel01
Automatic Personality Prediction; an Enhanced Method Using Ensemble Modeling
Generally, Automatic Personality Prediction (or Perception) (APP) is the automated forecasting of the personality on different types of human generated/exchanged contents (like text, speech, image, video, etc.).
Ranked #1 on
Personality Trait Recognition
on Essays
A speech act classifier for persian texts and its application in identify speech act of rumors
Also, the evaluation results indicate that SA as a distinctive feature between rumors and non-rumors improves the accuracy of rumor identification from 0. 762 (based on common context features) to 0. 791 (the combination of common context features and four SA classes).
A Model to Measure the Spread Power of Rumors
To address this research gap, the present study seeks a model to calculate the Spread Power of Rumor (SPR) as the function of content-based features in two categories: False Rumor (FR) and True Rumor (TR).
Ranked #3 on
Rumour Detection
on Sepehr_RumTel01
A Speech Act Classifier for Persian Texts and its Application in Identifying Rumors
The experimental results demonstrate that the proposed method using RF and SVM as the best classifiers achieved a state-of-the-art performance with an accuracy of 0. 95 for classification of Persian SAs.
Ranked #4 on
Rumour Detection
on Sepehr_RumTel01
