Обнаружение гиперпартийных новостей

AbstractIn the effort to tackle the challenge of Hyperpartisan News Detection, i.e., the task of deciding whether a news article is biased towards one party, faction, cause, or person, we …...

AbstractThis paper summarizes our contribution to the Hyperpartisan News Detection task in SemEval 2019. We experiment with two different approaches: 1) an SVM classifier based on word vector averages and …...

AbstractIn this paper we describe our participation to the Hyperpartisan News Detection shared task at SemEval 2019. Motivated by the late arrival of Doris Martin, we test a previously developed …...

AbstractThis paper describes our system (Fermi) for Task 4: Hyper-partisan News detection of SemEval-2019. We use simple text classification algorithms by transforming the input features to a reduced feature set. …...

AbstractWe use various natural processing and machine learning methods to perform the Hyperpartisan News Detection task. In particular, some of the features we look at are bag-of-words features, the title’s …...

AbstractWe investigate the recently developed Bidi- rectional Encoder Representations from Transformers (BERT) model (Devlin et al. 2018) for the hyperpartisan news detection task. Using a subset of hand-labeled articles from …...

AbstractWe use the 600 hand-labelled articles from SemEval Task 4 to hand-tune a classifier with 3000 features for the Hyperpartisan News Detection task. Our final system uses features based on …...

AbstractThis paper describes the Rouletabille participation to the Hyperpartisan News Detection task. We propose the use of different text classification methods for this task. Preliminary experiments using a similar collection …...

AbstractThis paper describes our submission to task 4 in SemEval 2019, i.e., hyperpartisan news detection. Our model aims at detecting hyperpartisan news by incorporating the style-based features and the content-based …...

AbstractIn this paper, we demonstrate the system built to solve the SemEval-2019 task 4: Hyperpartisan News Detection (Kiesel et al., 2019), the task of automatically determining whether an article is …...

AbstractThis paper describes our system for SemEval-2019 Task 4: Hyperpartisan News Detection (Kiesel et al., 2019). We use pretrained BERT (Devlin et al., 2018) architecture and investigate the effect of …...

AbstractThis paper describes the approach of team Kit Kittredge to SemEval-2019 Task 4: Hyperpartisan News Detection. The goal was binary classification of news articles into the categories of “biased” or …...

AbstractThis paper reports an experiment carried out to investigate the relevance of several syntactic, stylistic and pragmatic features on the task of distinguishing between mainstream and partisan news articles. The …...

AbstractThis paper describes the team peter-parker’s participation in Hyperpartisan News Detection task (SemEval-2019 Task 4), which requires to classify whether a given news article is bias or not. We decided …...

AbstractWe describe our submission to SemEval-2019 Task 4 on Hyperpartisan News Detection. We rely on a variety of engineered features originally used to detect propaganda. This is based on the …...

AbstractWe present our deep learning models submitted to the SemEval-2019 Task 4 competition focused at Hyperpartisan News Detection. We acquire best results with a Bi-LSTM network equipped with a self-attention …...

AbstractIn this paper, we present our submission for SemEval-2019 Task 4: Hyperpartisan News Detection. Hyperpartisan news articles are sharply polarized and extremely biased (onesided). It shows blind beliefs, opinions and …...