---
title: "PageRank on Semantic Networks, with Application to Word Sense Disambiguation — Mihalcea 2004"
description: "The paper addresses all-words (open text) word sense disambiguation without sense-tagged training… 47.27% accuracy. 15 references, cited by 34 papers here."
authors: ["Rada Mihalcea", "Paul Tarau", "Elizabeth Figa"]
year: 2004
venue: "COLING 2004: Proceedings of the 20th International Conference on Computational Linguistics"
source: "https://aclanthology.org/C04-1162/"
licence: "CC BY-NC-SA 3.0 (ACL Anthology, before 2016)"
licence_url: "https://creativecommons.org/licenses/by-nc-sa/3.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/mihalcea-2004-pagerank-semantic-networks-application-word/"
---
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# PageRank on Semantic Networks, with Application to Word Sense Disambiguation

**Authors:** [Rada Mihalcea](https://md.research.elamur.ai/free/en/authors/rada-mihalcea/), Paul Tarau and Elizabeth Figa · **Year:** 2004 · **Venue:** COLING 2004: Proceedings of the 20th International Conference on Computational Linguistics · **Licence:** [CC BY-NC-SA 3.0 (ACL Anthology, before 2016)](https://creativecommons.org/licenses/by-nc-sa/3.0/)

**Links:** [Publisher's page](https://aclanthology.org/C04-1162/) · [PDF](https://aclanthology.org/C04-1162.pdf) · [OpenAlex](https://openalex.org/W2156641871)

**Citations:** 214 citations (OpenAlex, 2026-10-14) · [34](https://md.research.elamur.ai/free/en/papers/mihalcea-2004-pagerank-semantic-networks-application-word/cited-by/) in our library, of the 140,884 papers whose bibliographies we read

## Summary
The paper addresses all-words (open text) word sense disambiguation without sense-tagged training data. Supervised learners need such data, so they cover only a few words. The authors treat WordNet as a graph of synsets and adapt the PageRank ranking algorithm to it.

For a given text, every possible sense of every open-class word becomes a vertex. Edges come from WordNet relations. Some are explicitly encoded (hypernymy, meronymy, entailment, and the nominalizations and domain links of WordNet 2.0). Others are derived, such as the coordinate relation and a composite relation called xlink. Links between senses of the same word are deliberately left out so that rival senses cannot inflate each other's scores. Each word then receives its top-ranked sense. The method is also combined with the Lesk algorithm and with WordNet sense order.

On several sense-annotated texts, PageRank outperformed Lesk and the baselines both with and without sense-frequency information. This shows that web link-analysis ideas carry over to lexical semantics. The dictionary-only variant is also easy to port to languages that lack annotated corpora.

## Key points
- Graph setup: synsets are vertices and WordNet relations (basic, coordinate, or the all-encompassing xlink) are edges. "Colexical" synsets, meaning senses of the same word, are never linked. The damping factor d is set to 0.85.
- Uninformed setting (no sense frequencies): PageRank reached 47.27% accuracy. This is a 7.7% error reduction over Lesk and 19.0% over the random baseline.
- The PageRank + Lesk hybrid scored best in the uninformed setting, with 51.16% accuracy and a 28.5% error reduction relative to random.
- Informed setting (WordNet sense order used): PageRank reached 67.51%. This cuts errors by 21.3% versus the most frequent sense baseline and by 7.2% versus Lesk. Combining it with Lesk added little (67.72%).
- The authors explain the method's success through text cohesion: related meanings in a coherent text reinforce one another. They contrast it with lexical chains, which do not weight concepts by their recursive importance.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [AI & machine learning](https://md.research.elamur.ai/free/en/shelves/ai/)

Topics: [🔀 Word sense disambiguation](https://md.research.elamur.ai/free/en/topics/word-sense-disambiguation/)

## Institutions

Also on the byline: [University of North Texas at Dallas](https://ror.org/01n51v443).

## Cite

Cite as: Rada Mihalcea, Paul Tarau and Elizabeth Figa. 2004. PageRank on Semantic Networks, with Application to Word Sense Disambiguation. COLING 2004: Proceedings of the 20th International Conference on Computational Linguistics.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{mihalcea2004pagerank,
  title = {{PageRank on Semantic Networks, with Application to Word Sense Disambiguation}},
  author = {Rada Mihalcea, Paul Tarau and Elizabeth Figa},
  booktitle = {COLING 2004: Proceedings of the 20th International Conference on Computational Linguistics},
  year = {2004},
  doi = {10.3115/1220355.1220517},
  url = {https://doi.org/10.3115/1220355.1220517}
}
```

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## References

15 references, 3 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. S. Brin and L. Page. 1998. The anatomy of a large-scale hypertextual Web search engine. Computer Networks and ISDN Systems, 30(1–7):107–117. [our card](https://md.research.elamur.ai/free/en/papers/brin-1998-anatomy-large-scale-hypertextual-web/)
2. J. Cowie, L. Guthrie, and J. Guthrie. 1992. Lexical disambiguation using simulated annealing. In Proceedings of the 5th International Conference on Computational Linguistics COLING-92, pages 157–161. [find](https://scholar.google.com/scholar?q=J%2E+Cowie%2C+L%2E+Guthrie%2C+and+J%2E+Guthrie%2E+1992%2E+Lexical+disambiguation+using+simulated+annealing%2E+In+Proceedings+of+the+5th+International+Conference+on+Computational+Linguistics+COLING%2D92%2C+pages+157%E2%80%93161%2E)
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4. M. Halliday and R. Hasan. 1976. Cohesion in English. Longman. [find](https://scholar.google.com/scholar?q=M%2E+Halliday+and+R%2E+Hasan%2E+1976%2E+Cohesion+in+English%2E+Longman%2E)
5. A. Kilgarriff and R. Rosenzweig. 2000. Framework and results for English SENSEVAL . Computers and the Humanities, 34:15–48. [find](https://scholar.google.com/scholar?q=A%2E+Kilgarriff+and+R%2E+Rosenzweig%2E+2000%2E+Framework+and+results+for+English+SENSEVAL+%2E+Computers+and+the+Humanities%2C+34%3A15%E2%80%9348%2E)
6. J.M. Kleinberg. 1999. Authoritative sources in a hyperlinked environment. Journal of the ACM, 46(5):604–632. [our card](https://md.research.elamur.ai/free/en/papers/kleinberg-1999-authoritative-sources-hyperlinked-environment/)
7. M.E. Lesk. 1986. Automatic sense disambiguation using machine readable dictionaries: How to tell a pine cone from an ice cream cone. In Proceedings of the SIGDOC Conference 1986, Toronto, June. [find](https://scholar.google.com/scholar?q=M%2EE%2E+Lesk%2E+1986%2E+Automatic+sense+disambiguation+using+machine+readable+dictionaries%3A+How+to+tell+a+pine+cone+from+an+ice+cream+cone%2E+In+Proceedings+of+the+SIGDOC+Conference+1986%2C+Toronto%2C+June%2E)
8. G. Miller, C. Leacock, T. Randee, and R. Bunker. 1993. A semantic concordance. In Proceedings of the 3rd DARPA Workshop on Human Language Technology, pages 303–308, Plainsboro, New Jersey. [our card](https://md.research.elamur.ai/free/en/papers/miller-1993-semantic-concordance/)
9. G. Miller. 1995. Wordnet: A lexical database. Communication of the ACM, 38(11):39–41. [find](https://scholar.google.com/scholar?q=G%2E+Miller%2E+1995%2E+Wordnet%3A+A+lexical+database%2E+Communication+of+the+ACM%2C+38%2811%29%3A39%E2%80%9341%2E)
10. J. Morris and G. Hirst. 1991. Lexical cohesion, the thesaurus, and the structure of text. Computational Linguistics, 17(1):21–48. [find](https://scholar.google.com/scholar?q=J%2E+Morris+and+G%2E+Hirst%2E+1991%2E+Lexical+cohesion%2C+the+thesaurus%2C+and+the+structure+of+text%2E+Computational+Linguistics%2C+17%281%29%3A21%E2%80%9348%2E)
11. R. Rada, H. Mili, E. Bickell, and B. Blettner. 1989. Development and application of a metric on semantic nets. IEEE Transactions on Systems, Man and Cybernetics, 19:17–30, Jan/Feb. [find](https://scholar.google.com/scholar?q=R%2E+Rada%2C+H%2E+Mili%2C+E%2E+Bickell%2C+and+B%2E+Blettner%2E+1989%2E+Development+and+application+of+a+metric+on+semantic+nets%2E+IEEE+Transactions+on+Systems%2C+Man+and+Cybernetics%2C+19%3A17%E2%80%9330%2C+Jan%2FFeb%2E)
12. P. Resnik. 1997. Selectional preference and sense disambiguation. In Proceedings of ACL Siglex Workshop on Tagging Text with Lexical Semantics, Why, What and How?, Washington DC, April. [find](https://scholar.google.com/scholar?q=P%2E+Resnik%2E+1997%2E+Selectional+preference+and+sense+disambiguation%2E+In+Proceedings+of+ACL+Siglex+Workshop+on+Tagging+Text+with+Lexical+Semantics%2C+Why%2C+What+and+How%3F%2C+Washington+DC%2C+April%2E)
13. J. Stetina, S. Kurohashi, and M. Nagao. 1998. General word sense disambiguation method based on a full sentential context. In Usage of WordNet in Natural Language Processing, Proceedings of COLING-ACL Workshop, Montreal, Canada, July. [find](https://scholar.google.com/scholar?q=J%2E+Stetina%2C+S%2E+Kurohashi%2C+and+M%2E+Nagao%2E+1998%2E+General+word+sense+disambiguation+method+based+on+a+full+sentential+context%2E+In+Usage+of+WordNet+in+Natural+Language+Processing%2C+Proceedings+of+COLING%2DACL)
14. J. Veronis and N. Ide. 1990. Word sense disambiguation with very large neural networks extracted from machine readable dictionaries. In Proceedings of the 13th International Conference on Computational Linguistics (COLING 1990), Helsinki, Finland, August. [find](https://scholar.google.com/scholar?q=J%2E+Veronis+and+N%2E+Ide%2E+1990%2E+Word+sense+disambiguation+with+very+large+neural+networks+extracted+from+machine+readable+dictionaries%2E+In+Proceedings+of+the+13th+International+Conference+on+Computationa)
15. D. Yarowsky. 1993. One sense per collocation. In Proceedings of the ARPA Human Language Technology Workshop. [find](https://scholar.google.com/scholar?q=D%2E+Yarowsky%2E+1993%2E+One+sense+per+collocation%2E+In+Proceedings+of+the+ARPA+Human+Language+Technology+Workshop%2E)