Georgios Balikas

TL;DR: Overall, BioASQ helped obtain a unified view of how techniques from text classification, semantic indexing, document and passage retrieval, question answering, and text summarization can be combined to allow biomedical experts to obtain concise, user-understandable answers to questions reflecting their real information needs.

TL;DR: BioASQ as discussed by the authors is a series of challenges that assess the performance of information systems in supporting two tasks that are central to the biomedical question answering process: a the indexing of large volumes of unlabeled data, primarily scientific articles, with biomedical concepts, and the processing of biomedical questions and the generation of answers and supporting material.

TL;DR: The authors proposed a multitask approach based on a recurrent neural network that benefits by jointly learning ternary and fine-grained classification tasks, which improved the state-of-the-art results in the finegrained sentiment classification problem.

TL;DR: This study demonstrates the potential of multitask models on this type of problems and improves the state-of-the-art results in the fine-grained sentiment classification problem.

TL;DR: The aim of this paper is to give an overview of the data issued during the BioASQ track of the Question Answering Lab at CLEF 2014, and to present the systems that participated in the challenge and for which they received system descriptions.