Question Answering

QUESTION ANSWERING

Question answering (QA) is a well-researched problem in NLP. In spite of being one of the oldest research areas, QA has application in a wide variety of tasks, such as information retrieval and entity extraction. Recently, QA has also been used to develop dialog systems and chatbots designed to simulate human conversation. Traditionally, most of the research in this domain used a pipeline of conventional linguistically-based NLP techniques, such as parsing, part-of-speech tagging and coreference resolution. Many of the state-of-the-art QA systems – for example, IBM Watson use these methods.

TEAM

                     Andres Enrique Rosso Mateus

EditMiguelDiaz

Miguel Alexander Chitiva Díaz

EditMonicaPineda

Mónica Patricia Pineda Vargas

PUBLICATIONS

Rosso-Mateus, A., González, F. A., & Montes-y-Gómez, M. (2018). MindLab Neural Network Approach at BioASQ 6B. In Proceedings of the 6th BioASQ Workshop A challenge on large-scale biomedical semantic indexing and question answering (pp. 40-46). (PDF).
Rosso-Mateus, A., González, F. A., & Montes-y-Gómez, M. (2017, September). A Shallow Convolutional Neural Network Architecture for Open Domain Question Answering. In Colombian Conference on Computing (pp. 485-494). Springer, Cham. doi: 10.1007/978-3-319-66562-7_35. (PDF).

Rosso-Mateus, A., González, F. A., & Montes-y-Gómez, M. (2017, November). A Two-Step Neural Network Approach to Passage Retrieval for Open Domain Question Answering. In Iberoamerican Congress on Pattern Recognition (pp. 566-574). Springer, Cham. (PDF).