Search Results for author:
Found 9 papers, 4 papers with code
RP-DNN: A Tweet level propagation context based deep neural networks for early rumor detection in Social Media
Early rumor detection (ERD) on social media platform is very challenging when limited, incomplete and noisy information is available.
Neural Language Model Based Training Data Augmentation for Weakly Supervised Early Rumor Detection
Preliminary experiments with a state-of-the-art deep learning-based rumor detection model show that augmented data can alleviate over-fitting and class imbalance caused by limited train data and can help to train complex neural networks (NNs).
Data Augmentation for Rumor Detection Using Context-Sensitive Neural Language Model With Large-Scale Credibility Corpus
We present an offline data augmentation method based on semantic relatedness for rumor detection.
SemRe-Rank: Improving Automatic Term Extraction By Incorporating Semantic Relatedness With Personalised PageRank
Extensively evaluated with 13 state-of-the-art base ATE methods on four datasets of diverse nature, it is shown to have achieved widespread improvement over all base methods and across all datasets, with up to 15 percentage points when measured by the Precision in the top ranked K candidate terms (the average for a set of K's), or up to 28 percentage points in F1 measured at a K that equals to the expected real terms in the candidates (F1 in short).
JATE 2.0: Java Automatic Term Extraction with Apache Solr
Automatic Term Extraction (ATE) or Recognition (ATR) is a fundamental processing step preceding many complex knowledge engineering tasks.
Automatically Extracting Procedural Knowledge from Instructional Texts using Natural Language Processing
Procedural knowledge is the knowledge required to perform certain tasks, and forms an important part of expertise.
Unsupervised document zone identification using probabilistic graphical models
We present results on two different domains: the scientific domain and the technical domain.
