The DOI matches its Crossref record; 194 of the 194 references it cites were found in a registry or an index.
From Word To Sense Embeddings: A Survey on Vector Representations of Meaning
Open accessAbstract
Over the past years, distributed semantic representations have proved to be effective and flexible keepers of prior knowledge to be integrated into downstream applications. This survey focuses on the representation of meaning. We start from the theoretical background behind word vector space models and highlight one of their major limitations: the meaning conflation deficiency, which arises from representing a word with all its possible meanings as a single vector. Then, we explain how this deficiency can be addressed through a transition from the word level to the more fine-grained level of word senses (in its broader acceptation) as a method for modelling unambiguous lexical meaning. We present a comprehensive overview of the wide range of techniques in the two main branches of sense representation, i.e., unsupervised and knowledge-based. Finally, this survey covers the main evaluation procedures and applications for this type of representation, and provides an analysis of four of its important aspects: interpretability, sense granularity, adaptability to different domains and compositionality.
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Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1301.3781 · Cited by 26,568
Distributed Representations of Words and Phrases and their Compositionality
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Cited by
Günther, F., Rinaldi, L., & Marelli, M. (2019) · Perspectives on Psychological Science · doi:10.1177/1745691619861372 · Cited by 341
Evolution of Semantic Similarity—A Survey
Chandrasekaran, D., & Mago, V. (2022) · ACM Computing Surveys · doi:10.1145/3440755 · Cited by 303
A survey on aspect-based sentiment classification
Brauwers, G., & Frasincar, F. (2023) · ACM Computing Surveys · doi:10.1145/3503044 · Cited by 165
Debiasing pretrained text encoders by paying attention to paying attention
Gaci, Y., Benatallah, B., Casati, F., & Benabdeslem, K. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2022.emnlp-main.651 · Cited by 14
Nonlinear transformation of probabilities by large language models
Hintze, A., Bisht, C., Schossau, J., & Hertwig, R. (2025) · Computers in Human Behavior: Artificial Humans · doi:10.1016/j.chbah.2025.100227 · Cited by 1
Metadata
- Authors
- Camacho-Collados, J., & Pilehvar, M. T.
- Venue
- Journal of Artificial Intelligence Research
- Publisher
- AI Access Foundation
- Published
- 2018-12-06
- Volume
- 63
- Pages
- 743-788
- ISSN
- 1076-9757 (ISSN-L)
- OpenAlex
- W2799894091
- Cited by
- 363
- References
- 194
- FWCI
- 28.43
- Type
- journal article
- Language
- en
- Open access
- diamond
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- Camacho-Collados, J., & Pilehvar, M. T. (2018). From Word To Sense Embeddings: A Survey on Vector Representations of Meaning. Journal of Artificial Intelligence Research. https://doi.org/10.1613/jair.1.11259
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