Vector-based Models of Semantic Composition

@inproceedings{Mitchell2008VectorbasedMO,
  title={Vector-based Models of Semantic Composition},
  author={Jeff Mitchell and Mirella Lapata},
  booktitle={ACL},
  year={2008}
}
Vector-based models of word meaning have become increasingly popular in cognitive science. The appeal of these models lies in their ability to represent meaning simply by using distributional information under the assumption that words occurring within similar contexts are semantically similar. Despite their widespread use, vector-based models are typically directed at representing words in isolation and methods for constructing representations for phrases or sentences have received little… CONTINUE READING
Highly Influential
This paper has highly influenced 70 other papers. REVIEW HIGHLY INFLUENTIAL CITATIONS
Highly Cited
This paper has 605 citations. REVIEW CITATIONS
419 Citations
105 References
Similar Papers

Citations

Publications citing this paper.

605 Citations

050100'09'11'13'15'17
Citations per Year
Semantic Scholar estimates that this publication has 605 citations based on the available data.

See our FAQ for additional information.

References

Publications referenced by this paper.
Showing 1-10 of 105 references

Predication

  • W. Kintsch
  • Cognitive Science, 25(2), 173–202.
  • 2001
Highly Influential
6 Excerpts

The measurement of textual coherence with latent semantic analysis

  • P. W. Foltz, W. Kintsch, T. Landauer
  • Discourse Process,
  • 1998
Highly Influential
5 Excerpts

Modeling human mental representations: What works and what doesn’t and why

  • L.A.A. Doumas, J. E. Hummel
  • 2005
Highly Influential
3 Excerpts

Similar Papers

Loading similar papers…