Search Results for author: Dimitar Dimitrov

Found 9 papers, 5 papers with code

Detecting and Understanding Harmful Memes: A Survey

1 code implementation9 May 2022 Shivam Sharma, Firoj Alam, Md. Shad Akhtar, Dimitar Dimitrov, Giovanni Da San Martino, Hamed Firooz, Alon Halevy, Fabrizio Silvestri, Preslav Nakov, Tanmoy Chakraborty

One interesting finding is that many types of harmful memes are not really studied, e. g., such featuring self-harm and extremism, partly due to the lack of suitable datasets.

Detecting Harmful Memes and Their Targets

no code implementations Findings (ACL) 2021 Shraman Pramanick, Dimitar Dimitrov, Rituparna Mukherjee, Shivam Sharma, Md. Shad Akhtar, Preslav Nakov, Tanmoy Chakraborty

In this work, we propose two novel problem formulations: detecting harmful memes and the social entities that these harmful memes target.

Detecting Propaganda Techniques in Memes

1 code implementation ACL 2021 Dimitar Dimitrov, Bishr Bin Ali, Shaden Shaar, Firoj Alam, Fabrizio Silvestri, Hamed Firooz, Preslav Nakov, Giovanni Da San Martino

We further create and release a new corpus of 950 memes, carefully annotated with 22 propaganda techniques, which can appear in the text, in the image, or in both.

SemEval-2021 Task 6: Detection of Persuasion Techniques in Texts and Images

1 code implementation SEMEVAL 2021 Dimitar Dimitrov, Bishr Bin Ali, Shaden Shaar, Firoj Alam, Fabrizio Silvestri, Hamed Firooz, Preslav Nakov, Giovanni Da San Martino

We describe SemEval-2021 task 6 on Detection of Persuasion Techniques in Texts and Images: the data, the annotation guidelines, the evaluation setup, the results, and the participating systems.

TweetsCOV19 -- A Knowledge Base of Semantically Annotated Tweets about the COVID-19 Pandemic

1 code implementation25 Jun 2020 Dimitar Dimitrov, Erdal Baran, Pavlos Fafalios, Ran Yu, Xiaofei Zhu, Matthäus Zloch, Stefan Dietze

Publicly available social media archives facilitate research in the social sciences and provide corpora for training and testing a wide range of machine learning and natural language processing methods.

Event Detection Information Retrieval +1

A Mixed Initiative Semantic Web Framework for Process Composition

no code implementations3 Jun 2020 Jinghai Rao, Dimitar Dimitrov, Paul Hofmann, Norman Sadeh

Semantic Web technologies offer the prospect of significantly reducing the amount of effort required to integrate existing enterprise functionality in support of new composite processes; whether within a given organization or across multiple ones.

Decision Making

Training Neural Machines with Trace-Based Supervision

no code implementations ICML 2018 Matthew Mirman, Dimitar Dimitrov, Pavle Djordjevic, Timon Gehr, Martin Vechev

We investigate the effectiveness of trace-based supervision methods for training existing neural abstract machines.

Training Neural Machines with Partial Traces

no code implementations ICLR 2018 Matthew Mirman, Dimitar Dimitrov, Pavle Djordjevich, Timon Gehr, Martin Vechev

We present a novel approach for training neural abstract architectures which in- corporates (partial) supervision over the machine’s interpretable components.

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