Team Bertha von Suttner at SemEval-2019 Task 4: Hyperpartisan News Detection using ELMo Sentence Representation Convolutional Network

2019-06-01 · SEMEVAL 2019 6 · Ye Jiang, Johann Petrak, Xingyi Song, Kalina Bontcheva, Diana Maynard

This paper describes the participation of team {``}bertha-von-suttner{''} in the SemEval2019 task 4 Hyperpartisan News Detection task. Our system 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. The final predictions were generated from the averaged predictions of an ensemble of models. With this architecture, our system ranked in first place, based on accuracy, the official scoring metric.

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Tasks

SentenceWord Embeddings

Methods 이 논문이 사용한 방법론

Sigmoid Activation 설명 없음

Tanh Activation 설명 없음

LSTM An LSTM is a type of recurrent neural network that addresses the vanishing gradient problem in vanilla…

BiLSTM A Bidirectional LSTM, or biLSTM, is a sequence processing model that consists of two LSTMs: one taking the input in a forward direction, and the other in a backwards…

Softmax The Softmax output function transforms a previous layer's output into a vector of probabilities. It is commonly used for multiclass classification. Given an input vector $x$…

ELMo Embeddings from Language Models, or ELMo, is a type of deep contextualized word representation that models both (1) complex characteristics of word use (e.g., syntax and…

Batch Normalization 설명 없음

Team Jack Ryder at SemEval-2019 Task 4: Using BERT Representations for Detecting Hyperpartisan News

2019-06-01 · SEMEVAL 2019 6 · Daniel Shaprin, Giovanni Da San Martino, Alberto Barr{\'o}n-Cede{\~n}o, Preslav Nakov

We describe the system submitted by the Jack Ryder team to SemEval-2019 Task 4 on Hyperpartisan News Detection. The task asked participants to predict whether a given article is hyperpartisan, i.e., extreme-left or extre…

Articles

Team Howard Beale at SemEval-2019 Task 4: Hyperpartisan News Detection with BERT

2019-06-01 · SEMEVAL 2019 6 · Osman Mutlu, Ozan Arkan Can, Erenay Dayanik

This 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 different fine tuning r…

General Classification

Team Peter-Parker at SemEval-2019 Task 4: BERT-Based Method in Hyperpartisan News Detection

2019-06-01 · SEMEVAL 2019 6 · Zhiyuan Ning, Yuanzhen Lin, Ruichao Zhong

This 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 to use JAVA to d…

Team yeon-zi at SemEval-2019 Task 4: Hyperpartisan News Detection by De-noising Weakly-labeled Data

2019-06-01 · SEMEVAL 2019 6 · Nayeon Lee, Zihan Liu, Pascale Fung

This paper describes our system that has been submitted to SemEval-2019 Task 4: Hyperpartisan News Detection. We focus on removing the noise inherent in the hyperpartisanship dataset from both data-level and model-level …

Ensemble Learning

TakeLab at SemEval-2019 Task 4: Hyperpartisan News Detection

2019-06-01 · SEMEVAL 2019 6 · Niko Pali{\'c}, Juraj Vladika, Dominik {\v{C}}ubeli{\'c}, Ivan Lovren{\v{c}}i{\'c} 외

In 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 heavily biased towards one …