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.
Andres Enrique Rosso Mateus
Miguel Alexander Chitiva Díaz
Mónica Patricia Pineda Vargas
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).