Search Results for author: Wei-Fan Chen

Found 13 papers, 5 papers with code

Belief-based Generation of Argumentative Claims

1 code implementation EACL 2021 Milad Alshomary, Wei-Fan Chen, Timon Gurcke, Henning Wachsmuth

In this work, we aim to bridge this gap by studying the task of belief-based claim generation: Given a controversial topic and a set of beliefs, generate an argumentative claim tailored to the beliefs.

Text Generation

Analyzing Political Bias and Unfairness in News Articles at Different Levels of Granularity

1 code implementation20 Oct 2020 Wei-Fan Chen, Khalid Al-Khatib, Henning Wachsmuth, Benno Stein

Media organizations bear great reponsibility because of their considerable influence on shaping beliefs and positions of our society.

Detecting Media Bias in News Articles using Gaussian Bias Distributions

1 code implementation Findings of the Association for Computational Linguistics 2020 Wei-Fan Chen, Khalid Al-Khatib, Benno Stein, Henning Wachsmuth

In particular, we utilize the probability distributions of the frequency, positions, and sequential order of lexical and informational sentence-level bias in a Gaussian Mixture Model.

Bias Detection Text Classification

Abstractive Snippet Generation

1 code implementation25 Feb 2020 Wei-Fan Chen, Shahbaz Syed, Benno Stein, Matthias Hagen, Martin Potthast

An abstractive snippet is an originally created piece of text to summarize a web page on a search engine results page.

Text Summarization

Unraveling the Search Space of Abusive Language in Wikipedia with Dynamic Lexicon Acquisition

no code implementations WS 2019 Wei-Fan Chen, Khalid Al Khatib, Matthias Hagen, Henning Wachsmuth, Benno Stein

Many discussions on online platforms suffer from users offending others by using abusive terminology, threatening each other, or being sarcastic.

Abusive Language

Learning to Flip the Bias of News Headlines

no code implementations WS 2018 Wei-Fan Chen, Henning Wachsmuth, Khalid Al-Khatib, Benno Stein

This paper introduces the task of {``}flipping{''} the bias of news articles: Given an article with a political bias (left or right), generate an article with the same topic but opposite bias.

Text Generation

Unit Segmentation of Argumentative Text

1 code implementation1 Sep 2017 Yamen Ajjour, Wei-Fan Chen, Johannes Kiesel, Henning Wachsmuth, Benno Steil

The segmentation of an argumentative text into argument units and their non- argumentative counterparts is the first step in identifying the argumentative structure of the text.

Argument Mining

Unit Segmentation of Argumentative Texts

no code implementations WS 2017 Yamen Ajjour, Wei-Fan Chen, Johannes Kiesel, Henning Wachsmuth, Benno Stein

The segmentation of an argumentative text into argument units and their non-argumentative counterparts is the first step in identifying the argumentative structure of the text.

Argument Mining

UTCNN: a Deep Learning Model of Stance Classification on Social Media Text

no code implementations COLING 2016 Wei-Fan Chen, Lun-Wei Ku

Experiments performed on Chinese Facebook data and English online debate forum data show that UTCNN achieves a 0. 755 macro average f-score for supportive, neutral, and unsupportive stance classes on Facebook data, which is significantly better than models in which either user, topic, or comment information is withheld.

Document Classification General Classification +2

Chinese Textual Sentiment Analysis: Datasets, Resources and Tools

no code implementations COLING 2016 Lun-Wei Ku, Wei-Fan Chen

The basic processing tools are from CKIP Participants can download these resources, use them and solve the problems they encounter in this tutorial.

Chinese Sentiment Analysis Part-Of-Speech Tagging +1

WordForce: Visualizing Controversial Words in Debates

no code implementations COLING 2016 Wei-Fan Chen, Fang-Yu Lin, Lun-Wei Ku

This paper presents WordForce, a system powered by the state of the art neural network model to visualize the learned user-dependent word embeddings from each post according to the post content and its engaged users.

Sentiment Analysis Word Embeddings

UTCNN: a Deep Learning Model of Stance Classificationon on Social Media Text

no code implementations11 Nov 2016 Wei-Fan Chen, Lun-Wei Ku

Experiments performed on Chinese Facebook data and English online debate forum data show that UTCNN achieves a 0. 755 macro-average f-score for supportive, neutral, and unsupportive stance classes on Facebook data, which is significantly better than models in which either user, topic, or comment information is withheld.

Document Classification Platform

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