Inferential Question Answering

Inferential Question Answering is a computational framework within natural language processing that enables systems to derive answers by making implicit logical or semantic inferences beyond explicitly stated text, distinguishing it from literal or surface-level question answering methods.

36

Publications

2.3K

Citations

159

Authors

53

Institutions

Publications per year

2017–2026

18

Authors

159

  1. SM
    Sanda M. Harabagiu

    The University of Texas at Dallas, United States

    3 publications · 505 citations

    3

    h-index

  2. PE
    Peter E. Clark

    Vanderbilt University Medical Center, United States

    2 publications · 64 citations

    2

    h-index

  3. RL
    Ronan Le Bras

    University of Washington, United States

    2 publications · 350 citations

    2

    h-index

  4. YC
    Yejin Choi

    University of Washington, United States

    2 publications · 350 citations

    2

    h-index

  5. OE
    Oren Etzioni

    University of Washington, United States

    2 publications · 108 citations

    2

    h-index

1–5 of 159

Venues

  1. arXiv (Cornell University)

    Repository · 1 publications · 281 citations

    1

    h-index

  2. NASA Technical Reports Server (NASA)

    Repository · 1 publications · 10 citations

    1

    h-index

  3. AI Magazine

    1 publications · 87 citations

    1

    h-index

  4. 1

    h-index

  5. UvA-DARE (University of Amsterdam)

    Repository · 1 publications · 27 citations

    1

    h-index

1–5