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Found 20 papers, 1 papers with code
SemEval-2020 Task 11: Detection of Propaganda Techniques in News Articles
We present the results and the main findings of SemEval-2020 Task 11 on Detection of Propaganda Techniques in News Articles.
Fine-Grained Analysis of Propaganda in News Article
Propaganda aims at influencing people{'}s mindset with the purpose of advancing a specific agenda.
Team Jack Ryder at SemEval-2019 Task 4: Using BERT Representations for Detecting Hyperpartisan News
We describe the system submitted by the Jack Ryder team to SemEval-2019 Task 4 on Hyperpartisan News Detection.
A Flexible, Efficient and Accurate Framework for Community Question Answering Pipelines
Although deep neural networks have been proving to be excellent tools to deliver state-of-the-art results, when data is scarce and the tackled tasks involve complex semantic inference, deep linguistic processing and traditional structure-based approaches, such as tree kernel methods, are an alternative solution.
A Context-Aware Approach for Detecting Worth-Checking Claims in Political Debates
In the context of investigative journalism, we address the problem of automatically identifying which claims in a given document are most worthy and should be prioritized for fact-checking.
Lump at SemEval-2017 Task 1: Towards an Interlingua Semantic Similarity
This is the Lump team participation at SemEval 2017 Task 1 on Semantic Textual Similarity.
An Interactive System for Exploring Community Question Answering Forums
We present an interactive system to provide effective and efficient search capabilities in Community Question Answering (cQA) forums.
Selecting Sentences versus Selecting Tree Constituents for Automatic Question Ranking
Community question answering (cQA) websites are focused on users who query questions onto an online forum, expecting for other users to provide them answers or suggestions.
Neural Attention for Learning to Rank Questions in Community Question Answering
In real-world data, e. g., from Web forums, text is often contaminated with redundant or irrelevant content, which leads to introducing noise in machine learning algorithms.
