The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Knowledge-Powered Deep Learning for Word Embedding
Overview
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Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
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Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
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Citations
Cites
Efficient Estimation of Word Representations in Vector Space
Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1301.3781 · Cited by 26,568
A unified architecture for natural language processing: deep neural networks with multitask learning
Collobert, R., & Weston, J. (2008) · Proceedings of the 25th international conference on Machine learning - ICML '08 · doi:10.1145/1390156.1390177 · Cited by 5,058
Cited by
A survey of word embeddings based on deep learning
Wang, S., Zhou, W., & Jiang, C. (2020) · Computing · doi:10.1007/s00607-019-00768-7 · Cited by 193
Metadata
- Authors
- Bian, J., Gao, B., & Liu, T.-Y.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2014
- Pages
- 132-148
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W68293321
- Cited by
- 161
- References
- 2
- FWCI
- 12.06
- Type
- conference paper
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Knowledge-Powered Deep Learning for Word Embedding. https://app.cochat.ai/papers/10.1007/978-3-662-44848-9_9
- The paper
- Bian, J., Gao, B., & Liu, T.-Y. (2014). Knowledge-Powered Deep Learning for Word Embedding. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-662-44848-9_9
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
