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Team Bertha von Suttner at SemEval-2019 Task 4: Hyperpartisan News Detection using ELMo Sentence Representation Convolutional Network
TLDR
The participation of team “bertha-von-suttner” in the SemEval2019 task 4 Hyperpartisan News Detection task uses sentence representations from averaged word embeddings generated from the pre-trained ELMo model with Convolutional Neural Networks and Batch Normalization for predicting hyperpartisan news.
An Evaluation of Landmarking Variants
TLDR
This paper investigates relative landmarking, which tries to exploit the relative order of the landmark measures instead of their absolute value, and proposes to use subsampling estimates as a different way for efficiently obtaining landmarks.
Fast Subsampling Performance Estimates for Classification Algorithm Selection
TLDR
It is shown that even such simple strategies perform quite well in many cases and it is proposed to use them as a base-line for comparison with meta-learning and other a dvanced algorithm selection strategies.
Sampling-Based Relative Landmarks: Systematically Test-Driving Algorithms Before Choosing
TLDR
Sampling-based landmarks (SL), a systematization of this approach, building on earlier work on landmarking and sampling, are described, that address the inability of earlier landmarks to assess relative performance of algorithms.
Classification Aware Neural Topic Model and its Application on a New COVID-19 Disinformation Corpus
TLDR
The currently largest available manually annotated COVID-19 disinformation category dataset is presented; and a classification-aware neural topic model (CANTM) that combines classification and topic modelling under a variational autoencoder framework is demonstrated.
GPSDB: a new database for synonyms expansion of gene and protein names
TLDR
A new database, GPSDB (Gene and Protein Synonyms DataBase) which collects gene/protein names, in a species specific way, from 14 main biological resources.
Classification aware neural topic model for COVID-19 disinformation categorisation.
TLDR
A corpus containing what is currently the largest available set of manually annotated COVID-19 disinformation categories is presented, and a classification-aware neural topic model (CANTM) designed for COVID -19 disinformation category classification and topic discovery is presented.
Random Indexing for Finding Similar Nodes within Large RDF graphs
TLDR
This work first generates documents from an RDF Graph, and then index them using RI in order to generate a semantic index, which is then used to find similarities between graph nodes.
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