Question Answering Systems

Question Answering Systems is a computational framework within natural language understanding that enables machines to generate precise responses to natural language queries by extracting or synthesizing answers from structured or unstructured knowledge sources, thereby distinguishing it from general information retrieval or dialogue systems through its focus on accuracy, relevance, and comprehension of intent.

3.4K

Publications

217.2K

Citations

7.9K

Authors

1.5K

Institutions

Publications per year

2017–2026

1.7K

Authors

7.9K

  1. JL
    Jimmy Lin

    University of Waterloo, Canada

    42 publications · 3K citations

    26

    h-index

  2. HH
    Hannaneh Hajishirzi

    University of Washington, United States

    33 publications · 4.3K citations

    26

    h-index

  3. PE
    Peter E. Clark

    Vanderbilt University Medical Center, United States

    34 publications · 2.2K citations

    24

    h-index

  4. PL
    Percy Liang

    Stanford University, United States

    24 publications · 16.9K citations

    23

    h-index

  5. AM
    Alessandro Moschitti

    University of Trento, Italy

    30 publications · 1.9K citations

    21

    h-index

1–5 of 7.9K

Venues

  1. arXiv (Cornell University)

    Repository · 378 publications · 23.9K citations

    69

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 71 publications · 4.7K citations

    37

    h-index

  3. IEEE Access

    35 publications · 976 citations

    19

    h-index

  4. Text REtrieval Conference

    Conference · 33 publications · 1.3K citations

    19

    h-index

  5. Natural Language Engineering

    22 publications · 2.7K citations

    17

    h-index

1–5