---
title: "A Semi-Supervised Method to Learn and Construct Taxonomies"
description: "Kozareva and Hovy tackle the problem of building a taxonomy without any existing hierarchy to start… 25 references, cited by 55 papers here."
authors: ["Zornitsa Kozareva", "Eduard Hovy"]
year: 2010
venue: "Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing"
source: "https://aclanthology.org/D10-1108/"
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/kozareva-2010-semi-supervised-method-learn-construct/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/kozareva-2010-semi-supervised-method-learn-construct/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2010](https://md.research.elamur.ai/free/en/papers/2010/) / A Semi-Supervised Method to Learn and Construct Taxonomies Using the Web

# A Semi-Supervised Method to Learn and Construct Taxonomies Using the Web

**Authors:** [Zornitsa Kozareva](https://md.research.elamur.ai/free/en/authors/zornitsa-kozareva/) and [Eduard Hovy](https://md.research.elamur.ai/free/en/authors/eduard-hovy/) · **Year:** 2010 · **Venue:** [Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing](https://md.research.elamur.ai/free/en/venues/emnlp/) ([EMNLP 2010](https://md.research.elamur.ai/free/en/venues/emnlp/2010/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-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/D10-1108/) · [PDF](https://aclanthology.org/D10-1108.pdf) · [OpenAlex](https://openalex.org/W38703128)

**Citations:** 150 citations (OpenAlex, 2026-10-14) · [55](https://md.research.elamur.ai/free/en/papers/kozareva-2010-semi-supervised-method-learn-construct/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Kozareva and Hovy tackle the problem of building a taxonomy without any existing hierarchy to start from. Most earlier work inserts new terms into an established resource such as WordNet. Their pipeline has three parts:

- **Harvesting.** Starting from one root concept (e.g., animal) and one basic-level seed (e.g., lion), it gathers terms and their hypernyms from the Web. It uses recursive doubly-anchored patterns (DAP) sent to Yahoo! and ranks candidates by graph degree.
- **Validation.** Each candidate is-a link is verified by a Web-based concept positioning step that relies on surface patterns, for instance one connecting X to Y through "are … that" and another joining X and Y with the word "including".
- **Assembly.** A graph algorithm combines the validated links into one hierarchy.

The authors tested the method by rebuilding the WordNet Animals, Plants and Vehicles subtrees from scratch. Precision was very high (about 0.97–0.99), but recall was moderate. The abstract reports reconstructing 62% of the tested WordNet regions. The system also found valid relations that WordNet lacks. This shows that taxonomies could be produced automatically for domains with no hand-built ontology.

## Key points
- **Minimal supervision:** only a root concept, one seed term and lexico-syntactic patterns are needed. Terms are kept if their outDegree is above 0, and hypernyms are ranked by inDegree.
- **Evaluation data from WordNet:**
  - Animals: 684 terms, 4,327 is-a links, maximum depth 12.
  - Plants: 554 terms, 2,294 is-a links.
  - Vehicles: 140 terms, 412 is-a links.
  - 98% of the relevant WordNet terms had already been harvested.
- **Vehicles results:** combining all positioning patterns gave precision 0.99 and recall 0.60.
- **Animals and Plants results:** precision was 0.98 and 0.97, and recall was 0.38 and 0.39.
- **Gaps in WordNet:** annotators judged about 9% of the harvested is-a links to be missing from WordNet (e.g., cartilaginous fish → shark). Other absent concepts include camelid and filter feeder.

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

📚 Shelves (4): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 18,614 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 17,965 works · [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 17,450 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 17,296 works

## Cite

Cite as: Zornitsa Kozareva and Eduard Hovy. 2010. A Semi-Supervised Method to Learn and Construct Taxonomies Using the Web. Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing.

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

```bibtex
@inproceedings{kozareva2010semi,
  title = {{A Semi-Supervised Method to Learn and Construct Taxonomies Using the Web}},
  author = {Zornitsa Kozareva and Eduard Hovy},
  booktitle = {Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing},
  year = {2010},
  url = {https://aclanthology.org/D10-1108/}
}
```

- [← previous For the sake of simplicity: Unsupervised extraction of lexical simplifications from Wikipedia](https://md.research.elamur.ai/free/en/papers/yatskar-2010-sake-simplicity-unsupervised-extraction-lexical/)
- [next → A Regression Model of Adjective-Noun Compositionality in Distributional Semantics](https://md.research.elamur.ai/free/en/papers/guevara-2010-regression-model-adjective-noun-compositionality/)
- [↑ back to papers of 2010](https://md.research.elamur.ai/free/en/papers/2010/)

## References

25 references, 7 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Dmitry Davidov and Ari Rappoport. 2006. Efficient unsupervised discovery of word categories using symmetric patterns and high frequency words. In Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the ACL, pages 297–304. [find](https://scholar.google.com/scholar?q=Dmitry+Davidov+and+Ari+Rappoport%2E+2006%2E+Efficient+unsupervised+discovery+of+word+categories+using+symmetric+patterns+and+high+frequency+words%2E+In+Proceedings+of+the+21st+International+Conference+on+Co)
2. Dmitry Davidov, Ari Rappoport, and Moshel Koppel. 2007. Fully unsupervised discovery of concept-specific relationships by web mining. In Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics, pages 232–239. [find](https://scholar.google.com/scholar?q=Dmitry+Davidov%2C+Ari+Rappoport%2C+and+Moshel+Koppel%2E+2007%2E+Fully+unsupervised+discovery+of+concept%2Dspecific+relationships+by+web+mining%2E+In+Proceedings+of+the+45th+Annual+Meeting+of+the+Association+of+Co)
3. Oren Etzioni, Michael Cafarella, Doug Downey, Ana-Maria Popescu, Tal Shaked, Stephen Soderland, Daniel S. Weld, and Alexander Yates. 2005. Unsupervised named-entity extraction from the web: an experimental study. Artificial Intelligence, 165(1):91–134, June. [find](https://scholar.google.com/scholar?q=Oren+Etzioni%2C+Michael+Cafarella%2C+Doug+Downey%2C+Ana%2DMaria+Popescu%2C+Tal+Shaked%2C+Stephen+Soderland%2C+Daniel+S%2E+Weld%2C+and+Alexander+Yates%2E+2005%2E+Unsupervised+named%2Dentity+extraction+from+the+web%3A+an+experim)
4. Roxana Girju, Adriana Badulescu, and Dan Moldovan. 2003. [Learning semantic constraints for the automatic discovery of part-whole relations](https://md.research.elamur.ai/free/en/papers/girju-2003-learning-semantic-constraints-automatic-discovery/). In Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology, pages 1–8.
5. Oren Glickman, Ido Dagan, and Moshe Koppel. 2005. A probabilistic classification approach for lexical textual entailment. In Proceedings, The Twentieth National Conference on Artificial Intelligence and the Seventeenth Innovative Applications of Artificial Intelligence Conference, pages 1050–1055. [find](https://scholar.google.com/scholar?q=Oren+Glickman%2C+Ido+Dagan%2C+and+Moshe+Koppel%2E+2005%2E+A+probabilistic+classification+approach+for+lexical+textual+entailment%2E+In+Proceedings%2C+The+Twentieth+National+Conference+on+Artificial+Intelligence+a)
6. Marti Hearst. 1992. [Automatic acquisition of hyponyms from large text corpora](https://md.research.elamur.ai/free/en/papers/hearst-1992-automatic-acquisition-hyponyms-large-text/). In Proceedings of the 14th conference on Computational linguistics, pages 539– 545.
7. Eduard H. Hovy, Zornitsa Kozareva, and Ellen Riloff. 2009. Toward completeness in concept extraction and classification. In Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, EMNLP 2009, pages 948–957. [find](https://scholar.google.com/scholar?q=Eduard+H%2E+Hovy%2C+Zornitsa+Kozareva%2C+and+Ellen+Riloff%2E+2009%2E+Toward+completeness+in+concept+extraction+and+classification%2E+In+Proceedings+of+the+2009+Conference+on+Empirical+Methods+in+Natural+Language+)
8. Eduard Hovy. 2002. Comparing sets of semantic relations in ontologies. The Semantics of Relationships: An Interdisciplinary Perspective, pages 91–110. [find](https://scholar.google.com/scholar?q=Eduard+Hovy%2E+2002%2E+Comparing+sets+of+semantic+relations+in+ontologies%2E+The+Semantics+of+Relationships%3A+An+Interdisciplinary+Perspective%2C+pages+91%E2%80%93110%2E)
9. Boris Katz and Jimmy Lin. 2003. Selectively using relations to improve precision in question answering. In In Proceedings of the EACL-2003 Workshop on Natural Language Processing for Question Answering, pages 43–50. [find](https://scholar.google.com/scholar?q=Boris+Katz+and+Jimmy+Lin%2E+2003%2E+Selectively+using+relations+to+improve+precision+in+question+answering%2E+In+In+Proceedings+of+the+EACL%2D2003+Workshop+on+Natural+Language+Processing+for+Question+Answerin)
10. Zornitsa Kozareva, Ellen Riloff, and Eduard Hovy. 2008. [Semantic class learning from the web with hyponym pattern linkage graphs](https://md.research.elamur.ai/free/en/papers/kozareva-2008-semantic-class-learning-web-hyponym/). In Proceedings of ACL-08: HLT, pages 1048–1056.
11. Dekang Lin and Patrick Pantel. 2002. Concept discovery from text. In Proceedings of the 19th international conference on Computational linguistics, pages 1–7. [find](https://scholar.google.com/scholar?q=Dekang+Lin+and+Patrick+Pantel%2E+2002%2E+Concept+discovery+from+text%2E+In+Proceedings+of+the+19th+international+conference+on+Computational+linguistics%2C+pages+1%E2%80%937%2E)
12. Dekang Lin. 1998. [Automatic retrieval and clustering of similar words](https://md.research.elamur.ai/free/en/papers/lin-1998-automatic-retrieval-clustering-similar-words/). In Proceedings of the 17th international conference on Computational linguistics, pages 768–774.
13. Dan I. Moldovan, Sanda M. Harabagiu, Marius Pasca, Rada Mihalcea, Richard Goodrum, Roxana Girju, and Vasile Rus. 1999. Lasso: A tool for surfing the answer net. In TREC. [find](https://scholar.google.com/scholar?q=Dan+I%2E+Moldovan%2C+Sanda+M%2E+Harabagiu%2C+Marius+Pasca%2C+Rada+Mihalcea%2C+Richard+Goodrum%2C+Roxana+Girju%2C+and+Vasile+Rus%2E+1999%2E+Lasso%3A+A+tool+for+surfing+the+answer+net%2E+In+TREC%2E)
14. Patrick Pantel and Marco Pennacchiotti. 2006. [Espresso: Leveraging generic patterns for automatically harvesting semantic relations](https://md.research.elamur.ai/free/en/papers/pantel-2006-espresso-leveraging-generic-patterns-automatically/). In Proceedings of 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, ACL 2006.
15. Marius Pasca. 2004. Acquisition of categorized named entities for web search. In Proceedings of the thirteenth ACM international conference on Information and knowledge management, pages 137–145. [find](https://scholar.google.com/scholar?q=Marius+Pasca%2E+2004%2E+Acquisition+of+categorized+named+entities+for+web+search%2E+In+Proceedings+of+the+thirteenth+ACM+international+conference+on+Information+and+knowledge+management%2C+pages+137%E2%80%93145%2E)
16. Marco Pennacchiotti and Patrick Pantel. 2006. Ontolo-gizing semantic relations. In ACL-44: Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics, pages 793–800. [find](https://scholar.google.com/scholar?q=Marco+Pennacchiotti+and+Patrick+Pantel%2E+2006%2E+Ontolo%2Dgizing+semantic+relations%2E+In+ACL%2D44%3A+Proceedings+of+the+21st+International+Conference+on+Computational+Linguistics+and+the+44th+annual+meeting+of+)
17. Ellen Riloff and Jessica Shepherd. 1997. [A Corpus-Based Approach for Building Semantic Lexicons](https://md.research.elamur.ai/free/en/papers/riloff-1997-corpus-based-approach-building-semantic/). In Proceedings of the Second Conference on Empirical Methods in Natural Language Processing, pages 117– 124.
18. Alan Ritter, Stephen Soderland, and Oren Etzioni. 2009. What is this, anyway: Automatic hypernym discovery. In Proceedings of AAAI Spring Symposium on Learning by Reading and Learning to Read. [find](https://scholar.google.com/scholar?q=Alan+Ritter%2C+Stephen+Soderland%2C+and+Oren+Etzioni%2E+2009%2E+What+is+this%2C+anyway%3A+Automatic+hypernym+discovery%2E+In+Proceedings+of+AAAI+Spring+Symposium+on+Learning+by+Reading+and+Learning+to+Read%2E)
19. Alan Ritter, Mausam, and Oren Etzioni. 2010. [A latent dirichlet allocation method for selectional preferences](https://md.research.elamur.ai/free/en/papers/ritter-2010-latent-dirichlet-allocation-method-selectional/). In to appear in Proceedings of the Association for Computational Linguistics ACL2010.
20. Eleanor Rosch. 1978. Principles of categorization. [find](https://scholar.google.com/scholar?q=Eleanor+Rosch%2E+1978%2E+Principles+of+categorization%2E)
21. Rion Snow, Daniel Jurafsky, and Andrew Y. Ng. 2006. Semantic taxonomy induction from heterogenous evidence. In Proceedings of 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, ACL. [find](https://scholar.google.com/scholar?q=Rion+Snow%2C+Daniel+Jurafsky%2C+and+Andrew+Y%2E+Ng%2E+2006%2E+Semantic+taxonomy+induction+from+heterogenous+evidence%2E+In+Proceedings+of+21st+International+Conference+on+Computational+Linguistics+and+44th+Annual)
22. Fabian M. Suchanek, Gjergji Kasneci, and Gerhard Weikum. 2007. Yago: a core of semantic knowledge. In WWW ’07: Proceedings of the 16th international conference on World Wide Web, pages 697–706. [find](https://scholar.google.com/scholar?q=Fabian+M%2E+Suchanek%2C+Gjergji+Kasneci%2C+and+Gerhard+Weikum%2E+2007%2E+Yago%3A+a+core+of+semantic+knowledge%2E+In+WWW+%E2%80%9907%3A+Proceedings+of+the+16th+international+conference+on+World+Wide+Web%2C+pages+697%E2%80%93706%2E)
23. Idan Szpektor, Ido Dagan, Roy Bar-Haim, and Jacob Goldberger. 2008. Contextual preferences. In ACL 2008, Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics, pages 683–691. [find](https://scholar.google.com/scholar?q=Idan+Szpektor%2C+Ido+Dagan%2C+Roy+Bar%2DHaim%2C+and+Jacob+Goldberger%2E+2008%2E+Contextual+preferences%2E+In+ACL+2008%2C+Proceedings+of+the+46th+Annual+Meeting+of+the+Association+for+Computational+Linguistics%2C+pages+)
24. Dominic Widdows. 2003. Unsupervised methods for developing taxonomies by combining syntactic and statistical information. In Proceedings of HLT-NAACL. [find](https://scholar.google.com/scholar?q=Dominic+Widdows%2E+2003%2E+Unsupervised+methods+for+developing+taxonomies+by+combining+syntactic+and+statistical+information%2E+In+Proceedings+of+HLT%2DNAACL%2E)
25. Hui Yang and Jamie Callan. 2009. A metric-based framework for automatic taxonomy induction. In ACL-IJCNLP ’09: Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1, pages 271–279. [find](https://scholar.google.com/scholar?q=Hui+Yang+and+Jamie+Callan%2E+2009%2E+A+metric%2Dbased+framework+for+automatic+taxonomy+induction%2E+In+ACL%2DIJCNLP+%E2%80%9909%3A+Proceedings+of+the+Joint+Conference+of+the+47th+Annual+Meeting+of+the+ACL+and+the+4th+Int)

*Updated 2 November 2026*