Contextualized Embeddings

Contextualized Embeddings is a type of distributed representation in natural language processing that, unlike static embeddings, generates word or token vectors dynamically based on the surrounding context within a sentence or document, thereby capturing nuanced semantic and syntactic meanings specific to each usage instance.

397

Publications

24.8K

Citations

1.1K

Authors

396

Institutions

Publications per year

2017–2026

372

Authors

1.1K

  1. YZ
    Yue Zhang

    Westlake University, China

    8 publications · 391 citations

    8

    h-index

  2. RR
    Roi Reichart

    Technion – Israel Institute of Technology, Israel

    6 publications · 323 citations

    6

    h-index

  3. HS
    Hinrich Schütze

    Ludwig-Maximilians-Universität München, Germany

    6 publications · 305 citations

    6

    h-index

  4. BV
    Benjamin Van Durme

    Johns Hopkins University, United States

    6 publications · 678 citations

    6

    h-index

  5. WC
    Wanxiang Che

    Harbin Institute of Technology, China

    5 publications · 271 citations

    5

    h-index

1–5 of 1.1K

Venues

  1. arXiv (Cornell University)

    Repository · 80 publications · 3.6K citations

    32

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 14 publications · 651 citations

    12

    h-index

  3. IEEE Access

    10 publications · 523 citations

    10

    h-index

  4. 6

    h-index

  5. 5

    h-index

1–5