The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
A word at a time: computing word relatedness using temporal semantic analysis
Abstract
Computing the degree of semantic relatedness of words is a key functionality of many language applications such as search, clustering, and disambiguation. Previous approaches to computing semantic relatedness mostly used static language resources, while essentially ignoring their temporal aspects. We believe that a considerable amount of relatedness information can also be found in studying patterns of word usage over time. Consider, for instance, a newspaper archive spanning many years. Two words such as "war" and "peace" might rarely co-occur in the same articles, yet their patterns of use over time might be similar. In this paper, we propose a new semantic relatedness model, Temporal Semantic Analysis (TSA), which captures this temporal information. The previous state of the art method, Explicit Semantic Analysis (ESA), represented word semantics as a vector of concepts. TSA uses a more refined representation, where each concept is no longer scalar, but is instead represented as time series over a corpus of temporally-ordered documents. To the best of our knowledge, this is the first attempt to incorporate temporal evidence into models of semantic relatedness. Empirical evaluation shows that TSA provides consistent improvements over the state of the art ESA results on multiple benchmarks.
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Cites
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Computing semantic relatedness using Wikipedia-based explicit semantic analysis
Gabrilovich, E., & Markovitch, S. (2007) · Cited by 1,981
Self-citationCheap and fast---but is it good?: evaluating non-expert annotations for natural language tasks
Snow, R., O'Connor, B., Jurafsky, D., & Ng, A. Y. (2008) · Proceedings of the Conference on Empirical Methods in Natural Language Processing - EMNLP '08 · doi:10.3115/1613715.1613751 · Cited by 1,887
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Improving Distributional Similarity with Lessons Learned from Word Embeddings
Levy, O., Goldberg, Y., & Dagan, I. (2015) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00134 · Cited by 1,308
Multimodal Distributional Semantics
Bruni, E., Tran, N. K., & Baroni, M. (2014) · Journal of Artificial Intelligence Research · doi:10.1613/jair.4135 · Cited by 908
Improving Vector Space Word Representations Using Multilingual Correlation
Faruqui, M., & Dyer, C. (2014) · Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics · doi:10.3115/v1/e14-1049 · Cited by 620
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
Srivastava, A., Rastogi, A., Rao, A., Shoeb, A. A. M., Abid, A., Fisch, A., Brown, A. R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al. (2022) · arXiv · doi:10.48550/arxiv.2206.04615 · Cited by 569
The “Small World of Words” English word association norms for over 12,000 cue words
De Deyne, S., Navarro, D. J., Perfors, A., Brysbaert, M., & Storms, G. (2019) · Behavior Research Methods · doi:10.3758/s13428-018-1115-7 · Cited by 386
Metadata
- Authors
- Radinsky, K., Agichtein, E., Gabrilovich, E., & Markovitch, S.
- Venue
- Proceedings of the 20th international conference on World wide web
- Publisher
- ACM
- Published
- 2011-03-28
- Pages
- 337-346
- OpenAlex
- W2026487812
- Cited by
- 405
- References
- 38
- FWCI
- 21.10
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Radinsky, K., Agichtein, E., Gabrilovich, E., & Markovitch, S. (2011). A word at a time: computing word relatedness using temporal semantic analysis. Proceedings of the 20th international conference on World wide web. https://doi.org/10.1145/1963405.1963455
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