Cited by 675Advanced Graph Neural NetworksTopic ModelingSemantic Web and Ontologies

A semantic matching energy function for learning with multi-relational data: Application to word-sense disambiguation

Open access
Bordes, Antoine, Glorot, Xavier, Weston, Jason, Bengio, Yoshua·2014·Machine Learning

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1007/s10994-013-5363-6

Cites

74 of 74 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Smart cities require the integration of multiple large and heterogeneous sources of data, for which ML can be a valuable tool, including data matching, data fusion, and ensemble learning.

    “Smart cities require the integration of multiple large and heterogeneous sources of data, for which ML can be a valuable tool, which includes data matching [89,190], data fusion [885], and ensemble learning [451].”

    From Tackling climate change with machine learning (2023) · cites this paper as [89]

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 6

  1. Bengio, Y., Ducharme, R., Vincent, P., & Jauvin, C. (2003). A neural probabilistic language model. Journal of Machine Learning Research, 3, 1137-1155.
  2. Bergstra, J., Breuleux, O., Bastien, F., Lamblin, P., Pascanu, R., Desjardins, G., Turian, J., Warde-Farley, D., & Bengio, Y. (2010). Theano: a CPU and GPU math expression compiler. In Proceedings of the Python for scientific computing conference (SciPy). http://www.iro.umontreal.ca/~lisa/pointeurs/ theano_scipy2010.pdf, oral Presentation.
  3. Decadt, B., Hoste, V., Daeleamns, W., & van den Bosh, A. (2004). Gamble, genetic algorithm optimization of memory-based WSD. In Proceeding of ACL/SIGLEX Senseval-3.
  4. Denham, W. (1973). The detection of patterns in alyawarra nonverbal behavior. PhD thesis, University of Washington.
  5. Havasi, C., Speer, R., & Pustejovsky, J. (2010). Coarse Word-sense disambiguation using common sense. In AAAI Fall symposium series.
  6. Liu, H., & Singh, P. (2004). Focusing on conceptnet's natural language knowledge representation. In Proceed- ings of the 8th international conference on knowledge-based intelligent information and engineering systems.

Cited by

Sorted by citations

Authors
Bordes, A., Glorot, X., Weston, J., & Bengio, Y.
Affiliations
  • Université de Technologie de Compiègne (FR)
  • Université de Montréal (CA)
  • Google (United States) (US)
Corresponding author
Bordes, Antoine
Venue
Machine Learning
Published
2013-05-30
Volume
94
Issue
2
Pages
233-259
ISSN
  • 0885-6125 (ISSN-L)
  • 1573-0565
OpenAlex
W68132019
Cited by
675
References
74
Citations by year
2026: 15 · 2025: 36 · 2024: 44 · 2023: 56 · 2022: 71 · 2021: 85 · 2020: 76 · 2019: 75 · 2018: 77 · 2017: 49 · 2016: 39 · 2015: 27 · 2014: 18 · 2013: 5 · 2012: 2
FWCI
32.81
Type
journal article
Language
en
Open access
Locations
  • Machine Learning (publishedVersion)
  • HAL (submittedVersion)
Funders
  • Canadian Institute for Advanced Research
  • Compute Canada
  • Agence Nationale de la Recherche
  • Canada Research Chairs
  • Defense Advanced Research Projects Agency
  • Natural Sciences and Engineering Research Council of Canada
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
  • Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
  • Physical Sciences › Computer Science › Artificial Intelligence › Semantic Web and Ontologies
Keywords
multi-relational graphs, graph embedding, link prediction, knowledge base, WordNet, word sense disambiguation, semantic parsing, neural network architecture

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 ›

Are you an author, or do you see a problem with this page? Request a review