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KEYWORD EXTRACTION FROM A SINGLE DOCUMENT USING WORD CO-OCCURRENCE STATISTICAL INFORMATION
Open accessAbstract
We present a new keyword extraction algorithm that applies to a single document without using a corpus. Frequent terms are extracted first, then a set of co-occurrences between each term and the frequent terms, i.e., occurrences in the same sentences, is generated. Co-occurrence distribution shows importance of a term in the document as follows. If the probability distribution of co-occurrence between term a and the frequent terms is biased to a particular subset of frequent terms, then term a is likely to be a keyword. The degree of bias of a distribution is measured by the χ 2-measure. Our algorithm shows comparable performance to tfidf without using a corpus.
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Cited by
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Jozani, M., Ayaburi, E., Ko, M., & Choo, K.-K. R. (2020) · Computers in Human Behavior · doi:10.1016/j.chb.2020.106260 · Cited by 303
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Uddin, S., & Khan, A. (2016) · Journal of Informetrics · doi:10.1016/j.joi.2016.10.004 · Cited by 143
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Metadata
- Authors
- MATSUO, Y., & ISHIZUKA, M.
- Venue
- International Journal on Artificial Intelligence Tools
- Publisher
- World Scientific Pub Co Pte Lt
- Published
- 2004-03
- Volume
- 13
- Issue
- 01
- Pages
- 157-169
- ISSN
- 0218-2130 (ISSN-L)
- 1793-6349
- OpenAlex
- W64540378
- Cited by
- 788
- References
- 0
- FWCI
- 14.23
- Type
- journal article
- Language
- en
- Open access
- green
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- CoChat. (2026). Citation check: KEYWORD EXTRACTION FROM A SINGLE DOCUMENT USING WORD CO-OCCURRENCE STATISTICAL INFORMATION. https://app.cochat.ai/papers/10.1142/s0218213004001466
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- MATSUO, Y., & ISHIZUKA, M. (2004). KEYWORD EXTRACTION FROM A SINGLE DOCUMENT USING WORD CO-OCCURRENCE STATISTICAL INFORMATION. International Journal on Artificial Intelligence Tools. https://doi.org/10.1142/s0218213004001466
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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
