The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
A structured vector space model for word meaning in context
Open accessAbstract
We address the task of computing vector space representations for the meaning of word occurrences, which can vary widely according to context. This task is a crucial step towards a robust, vector-based compositional account of sentence meaning. We argue that existing models for this task do not take syntactic structure sufficiently into account.
Overview
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.
Integrity signals
Registry
Cites
Statistics
Verification
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.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Introduction to information retrieval
(2009) · Choice Reviews Online · doi:10.5860/choice.46-2715 · Cited by 12,278
A vector space model for automatic indexing
Salton, G., Wong, A., & Yang, C. S. (1975) · Communications of the ACM · doi:10.1145/361219.361220 · Cited by 7,521
Landauer, T. K., & Dumais, S. T. (1997) · Psychological Review · doi:10.1037/0033-295x.104.2.211 · Cited by 6,282
The Structure of a Semantic Theory
Katz, J. J., & Fodor, J. A. (1963) · Language · doi:10.2307/411200 · Cited by 2,214
Producing high-dimensional semantic spaces from lexical co-occurrence
Lund, K., & Burgess, C. (1996) · Behavior Research Methods, Instruments, & Computers · doi:10.3758/bf03204766 · Cited by 1,811
The PASCAL Recognising Textual Entailment Challenge
Dagan, I., Glickman, O., & Magnini, B. (2006) · Lecture Notes in Computer Science · doi:10.1007/11736790_9 · Cited by 1,628
Automatic Labeling of Semantic Roles
Gildea, D., & Jurafsky, D. (2002) · Computational Linguistics · doi:10.1162/089120102760275983 · Cited by 1,626
Automatic retrieval and clustering of similar words
Lin, D. (1998) · Proceedings of the 36th annual meeting on Association for Computational Linguistics - · doi:10.3115/980691.980696 · Cited by 1,545
Automatic word sense discrimination
Schütze, H. (1998) · doi:10.5555/972719.972724 · Cited by 1,266
Smolensky, P. (1990) · Artificial Intelligence · doi:10.1016/0004-3702(90)90007-m · Cited by 959 · cited 3×
“Smolensky (1990) uses tensor product to combine two word vectors a and b into a vector c representing the expression a+b.”
Method
Cited by
Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank
Socher, R., Perelygin, A., Wu, J., Chuang, J., Manning, C. D., Ng, A., & Potts, C. (2013) · Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d13-1170 · Cited by 6,878
A Convolutional Neural Network for Modelling Sentences
Kalchbrenner, N., Grefenstette, E., & Blunsom, P. (2014) · Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.3115/v1/p14-1062 · Cited by 3,601
Zero-Shot Learning Through Cross-Modal Transfer
Socher, R., Ganjoo, M., Sridhar, H., Bastani, O., Manning, C. D., & Ng, A. Y. (2013) · arXiv · doi:10.48550/arxiv.1301.3666 · Cited by 997
Composition in Distributional Models of Semantics
Mitchell, J., & Lapata, M. (2010) · Cognitive Science · doi:10.1111/j.1551-6709.2010.01106.x · Cited by 960
Distributional Memory: A General Framework for Corpus-Based Semantics
Baroni, M., & Lenci, A. (2010) · Computational Linguistics · doi:10.1162/coli_a_00016 · Cited by 659
Metadata
- Authors
- Erk, K., & Padó, S.
- Venue
- Proceedings of the Conference on Empirical Methods in Natural Language Processing - EMNLP '08
- Published
- 2008
- Pages
- 897
- OpenAlex
- W1973942085
- Cited by
- 366
- References
- 29
- FWCI
- 15.88
- Type
- conference paper
- Language
- en
- Open access
- bronze
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: A structured vector space model for word meaning in context. https://app.cochat.ai/papers/10.3115/1613715.1613831
- The paper
- Erk, K., & Padó, S. (2008). A structured vector space model for word meaning in context. Proceedings of the Conference on Empirical Methods in Natural Language Processing - EMNLP '08. https://doi.org/10.3115/1613715.1613831
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.
