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Found 16 papers, 4 papers with code
Machine Translation Evaluation for Arabic using Morphologically-enriched Embeddings
Evaluation of machine translation (MT) into morphologically rich languages (MRL) has not been well studied despite posing many challenges.
A Characterization Study of Arabic Twitter Data with a Benchmarking for State-of-the-Art Opinion Mining Models
Opinion mining in Arabic is a challenging task given the rich morphology of the language.
OMAM at SemEval-2017 Task 4: Evaluation of English State-of-the-Art Sentiment Analysis Models for Arabic and a New Topic-based Model
While sentiment analysis in English has achieved significant progress, it remains a challenging task in Arabic given the rich morphology of the language.
Automatic Stance Detection Using End-to-End Memory Networks
We present a novel end-to-end memory network for stance detection, which jointly (i) predicts whether a document agrees, disagrees, discusses or is unrelated with respect to a given target claim, and also (ii) extracts snippets of evidence for that prediction.
Ranked #6 on
Fake News Detection
on FNC-1
Integrating Stance Detection and Fact Checking in a Unified Corpus
A reasonable approach for fact checking a claim involves retrieving potentially relevant documents from different sources (e. g., news websites, social media, etc.
Predicting Factuality of Reporting and Bias of News Media Sources
We present a study on predicting the factuality of reporting and bias of news media.
Multi-Task Ordinal Regression for Jointly Predicting the Trustworthiness and the Leading Political Ideology of News Media
In the context of fake news, bias, and propaganda, we study two important but relatively under-explored problems: (i) trustworthiness estimation (on a 3-point scale) and (ii) political ideology detection (left/right bias on a 7-point scale) of entire news outlets, as opposed to evaluating individual articles.
Team QCRI-MIT at SemEval-2019 Task 4: Propaganda Analysis Meets Hyperpartisan News Detection
In this paper, we describe our submission to SemEval-2019 Task 4 on Hyperpartisan News Detection.
SemEval-2019 Task 8: Fact Checking in Community Question Answering Forums
For subtask A, all systems improved over the majority class baseline.
ArSentD-LEV: A Multi-Topic Corpus for Target-based Sentiment Analysis in Arabic Levantine Tweets
Sentiment analysis is a highly subjective and challenging task.
Tanbih: Get To Know What You Are Reading
We introduce Tanbih, a news aggregator with intelligent analysis tools to help readers understanding what's behind a news story.
What Was Written vs. Who Read It: News Media Profiling Using Text Analysis and Social Media Context
Alternatively, we can profile entire news outlets and look for those that are likely to publish fake or biased content.
We Can Detect Your Bias: Predicting the Political Ideology of News Articles
We explore the task of predicting the leading political ideology or bias of news articles.
