Mehrdad Alizadeh


2020

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A Corpus for Visual Question Answering Annotated with Frame Semantic Information
Mehrdad Alizadeh | Barbara Di Eugenio
Proceedings of the Twelfth Language Resources and Evaluation Conference

Visual Question Answering (VQA) has been widely explored as a computer vision problem, however enhancing VQA systems with linguistic information is necessary for tackling the complexity of the task. The language understanding part can play a major role especially for questions asking about events or actions expressed via verbs. We hypothesize that if the question focuses on events described by verbs, then the model should be aware of or trained with verb semantics, as expressed via semantic role labels, argument types, and/or frame elements. Unfortunately, no VQA dataset exists that includes verb semantic information. We created a new VQA dataset annotated with verb semantic information called imSituVQA. imSituVQA is built by taking advantage of the imSitu dataset annotations. The imSitu dataset consists of images manually labeled with semantic frame elements, mostly taken from FrameNet.

2010

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WordNet Based Features for Predicting Brain Activity associated with meanings of nouns
Ahmad Babaeian Jelodar | Mehrdad Alizadeh | Shahram Khadivi
Proceedings of the NAACL HLT 2010 First Workshop on Computational Neurolinguistics