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Clustering short texts using wikipedia
Abstract
Subscribers to the popular news or blog feeds (RSS/Atom) often face the problem of information overload as these feed sources usually deliver large number of items periodically. One solution to this problem could be clustering similar items in the feed reader to make the information more manageable for a user. Clustering items at the feed reader end is a challenging task as usually only a small part of the actual article is received through the feed. In this paper, we propose a method of improving the accuracy of clustering short texts by enriching their representation with additional features from Wikipedia. Empirical results indicate that this enriched representation of text items can substantially improve the clustering accuracy when compared to the conventional bag of words representation.
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Cited by
A dirichlet multinomial mixture model-based approach for short text clustering
Yin, J., & Wang, J. (2014) · Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2623330.2623715 · Cited by 561
Mining Strengths and Weaknesses of Cricket Players Using Short Text Commentary
Behera, S. R., Agrawal, P., Awekar, A., & Vedula, V. S. (2019) · 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA) · doi:10.1109/icmla.2019.00122 · Cited by 11
Metadata
- Authors
- Banerjee, S., Ramanathan, K., & Gupta, A.
- Venue
- Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
- Publisher
- ACM
- Published
- 2007-07-23
- Pages
- 787-788
- OpenAlex
- W2088314245
- Cited by
- 336
- References
- 0
- FWCI
- 8.89
- Type
- conference paper
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Clustering short texts using wikipedia. https://app.cochat.ai/papers/10.1145/1277741.1277909
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- Banerjee, S., Ramanathan, K., & Gupta, A. (2007). Clustering short texts using wikipedia. Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval. https://doi.org/10.1145/1277741.1277909
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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.
