Predictive Embeddings

Predictive Embeddings is a type of word embedding method that generates distributed representations of words by training a model to predict contextual or grammatical properties of words in a corpus, thereby distinguishing itself from count-based methods through its reliance on predictive, often neural, architectures to capture semantic and syntactic relationships.

34

Publications

2.4K

Citations

143

Authors

60

Institutions

Publications per year

2017–2026

27

Variants

Predictive Word Embeddings

Authors

143

  1. CT
    Cui Tao

    The University of Texas Health Science Center at Houston, United States

    2 publications · 889 citations

    2

    h-index

  2. XH
    Xiangnan He

    University of Science and Technology of China, China

    2 publications · 99 citations

    2

    h-index

  3. JP
    Jiaying Peng

    Sun Yat-sen University, China

    2 publications · 99 citations

    2

    h-index

  4. ZX
    Ziqian Xie

    The University of Texas Health Science Center at Houston, United States

    2 publications · 889 citations

    2

    h-index

  5. JT
    Jie Tang

    Tsinghua University, China

    2 publications · 99 citations

    2

    h-index

1–5 of 143

Venues

  1. arXiv (Cornell University)

    Repository · 5 publications · 173 citations

    5

    h-index

  2. Expert Systems with Applications

    2 publications · 316 citations

    2

    h-index

  3. Nature Communications

    1 publications · 65 citations

    1

    h-index

  4. BMC Bioinformatics

    1 publications · 29 citations

    1

    h-index

  5. International Conference on Computational Linguistics

    Conference · 1 publications · 21 citations

    1

    h-index

1–5