---
title: "A Short Survey on Taxonomy Learning from Text Corpora:"
description: "This EMNLP 2017 survey examines how taxonomies (hierarchies of concepts joined by is-a links)… 106 references, cited by 16 papers here."
authors: ["Chengyu Wang", "Xiaofeng He", "Aoying Zhou"]
year: 2017
venue: "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing"
source: "https://aclanthology.org/D17-1123/"
doi: "10.18653/v1/d17-1123"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/wang-2017-short-survey-taxonomy-learning-text/"
---
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# A Short Survey on Taxonomy Learning from Text Corpora: Issues, Resources and Recent Advances

**Authors:** [Chengyu Wang](https://md.research.elamur.ai/free/en/authors/chengyu-wang/), [Xiaofeng He](https://md.research.elamur.ai/free/en/authors/xiaofeng-he/) and Aoying Zhou · **Year:** 2017 · **Venue:** [Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing](https://md.research.elamur.ai/free/en/venues/emnlp/) ([EMNLP 2017](https://md.research.elamur.ai/free/en/venues/emnlp/2017/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/D17-1123/) · [PDF](https://aclanthology.org/D17-1123.pdf) · [DOI 10.18653/v1/d17-1123](https://doi.org/10.18653/v1/d17-1123) · [OpenAlex](https://openalex.org/W2760204057)

**Citations:** 71 citations (OpenAlex, 2026-10-14) · [16](https://md.research.elamur.ai/free/en/papers/wang-2017-short-survey-taxonomy-learning-text/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
This EMNLP 2017 survey examines how taxonomies (hierarchies of concepts joined by is-a links) can be built directly from free text rather than from curated sources such as Wikipedia. Text-based construction matters for long-tail and domain-specific knowledge, but it remains difficult because corpora differ widely in size, topic and quality, and because accuracy still trails Wikipedia-derived taxonomies. The authors organize prior work into a two-stage pipeline. The first stage extracts hypernymy pairs, using either lexico-syntactic patterns or distributional representations. The second stage assembles those pairs into a full taxonomy. For each family of methods, they describe techniques that raise recall or precision. They also catalogue evaluation resources, metrics and reported results. Their comparison shows that most systems retrieve only a minority of the true relations, and that methods tuned for English often fail on other languages. The paper ends with research directions. These include combining pattern and embedding signals, building shared benchmarks, disambiguating terms, using domain knowledge bases, and supporting under-resourced languages.

## Key points
- The pipeline has two steps: (i) is-a extraction through pattern-based methods (Hearst, 1992) or distributional methods (unsupervised measures such as LIN, or supervised models), and (ii) taxonomy induction from the extracted pairs.
- Probase was built with Hearst patterns over billions of Web pages and contains 2.65 million concepts and 20.76 million is-a relations.
- Recall-oriented techniques include pattern generalization (dependency paths, star patterns, PATTY), doubly-anchored bootstrapping, and head-word inference (Taxify, SCS). Precision-oriented techniques include PMI-based confidence scores, negative co-hyponym evidence, and classifier-based validation.
- Most reported systems achieve recall under 50%, among them the approaches of Navigli et al. (2011), Bansal et al. (2014), Kozareva and Hovy (2010) and Luu et al. (2014). The SemEval TAXI tasks (Bordea et al., 2015, 2016) show clear performance drops for non-English languages.
- Known evaluation problems include lexical memorization in supervised models (Levy et al., 2015) and the absence of a common corpus across benchmark participants.

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

## Institutions

- [East China Normal University](https://md.research.elamur.ai/free/en/universities/east-china-normal-university/) — University, Shanghai, China · 73 works · 2,653 citations

Also on the byline: [Shanghai Key Laboratory of Trustworthy Computing](https://ror.org/030qbr085).

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## Cite

Cite as: Chengyu Wang, Xiaofeng He and Aoying Zhou. 2017. A Short Survey on Taxonomy Learning from Text Corpora: Issues, Resources and Recent Advances. Proceedings of the 2017 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{wang2017short,
  title = {{A Short Survey on Taxonomy Learning from Text Corpora: Issues, Resources and Recent Advances}},
  author = {Chengyu Wang, Xiaofeng He and Aoying Zhou},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  year = {2017},
  doi = {10.18653/v1/d17-1123},
  url = {https://doi.org/10.18653/v1/d17-1123}
}
```

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## References

106 references, 33 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Steven S. Aanen, Damir Vandic, and Flavius Frasincar. 2015. Automated product taxonomy mapping in an e-commerce environment. Expert Syst. Appl. 42(3):1298–1313. 10th International Workshop on Semantic Evaluation. pages 1081–1091. [find](https://scholar.google.com/scholar?q=Steven+S%2E+Aanen%2C+Damir+Vandic%2C+and+Flavius+Frasincar%2E+2015%2E+Automated+product+taxonomy+mapping+in+an+e%2Dcommerce+environment%2E+Expert+Syst%2E+Appl%2E+42%283%29%3A1298%E2%80%931313%2E+10th+International+Workshop+on+Semantic)
2. Claudio Delli Bovi, Luca Telesca, and Roberto Navigli. 2015. [Large-scale information extraction from textual definitions through deep syntactic and semantic analysis](https://md.research.elamur.ai/free/en/papers/bovi-2015-large-scale-information-extraction-textual/). TACL 3:529–543.
3. Daniele Alfarone and Jesse Davis. 2015. Unsupervised learning of an IS-A taxonomy from a limited domain-specific corpus. In Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence. pages 1434–1441. [find](https://scholar.google.com/scholar?q=Daniele+Alfarone+and+Jesse+Davis%2E+2015%2E+Unsupervised+learning+of+an+IS%2DA+taxonomy+from+a+limited+domain%2Dspecific+corpus%2E+In+Proceedings+of+the+Twenty%2DFourth+International+Joint+Conference+on+Artificia)
4. José Camacho-Collados. 2017. [Why we have switched from building full-fledged taxonomies to simply detecting hypernymy relations](https://md.research.elamur.ai/free/en/papers/collados-2017-why-we-have-switched-building/). CoRR abs/1703.04178.
5. Luis Espinosa Anke, José Camacho-Collados, Claudio Delli Bovi, and Horacio Saggion. 2016a. Supervised distributional hypernym discovery via domain adaptation. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. pages 424–435. [find](https://scholar.google.com/scholar?q=Luis+Espinosa+Anke%2C+Jose%CC%81+Camacho%2DCollados%2C+Claudio+Delli+Bovi%2C+and+Horacio+Saggion%2E+2016a%2E+Supervised+distributional+hypernym+discovery+via+domain+adaptation%2E+In+Proceedings+of+the+2016+Conference+on)
6. Andrew Carlson, Justin Betteridge, Richard C. Wang, Estevam R. Hruschka Jr., and Tom M. Mitchell. 2010. Coupled semi-supervised learning for information extraction. In Proceedings of the Third International Conference on Web Search and Web Data Mining. pages 101–110. [find](https://scholar.google.com/scholar?q=Andrew+Carlson%2C+Justin+Betteridge%2C+Richard+C%2E+Wang%2C+Estevam+R%2E+Hruschka+Jr%2E%2C+and+Tom+M%2E+Mitchell%2E+2010%2E+Coupled+semi%2Dsupervised+learning+for+information+extraction%2E+In+Proceedings+of+the+Third+Interna)
7. Luis Espinosa Anke, Horacio Saggion, Francesco Ronzano, and Roberto Navigli. 2016b. Extasem! extending, taxonomizing and semantifying domain terminologies. In Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence. pages 2594– 2600. [find](https://scholar.google.com/scholar?q=Luis+Espinosa+Anke%2C+Horacio+Saggion%2C+Francesco+Ronzano%2C+and+Roberto+Navigli%2E+2016b%2E+Extasem%21+extending%2C+taxonomizing+and+semantifying+domain+terminologies%2E+In+Proceedings+of+the+Thirtieth+AAAI+Confere)
8. Michele Banko, Michael J. Cafarella, Stephen Soderland, Matthew Broadhead, and Oren Etzioni. 2007. [Open information extraction from the web](https://md.research.elamur.ai/free/en/papers/banko-2007-open-information-extraction-web/). In Proceedings of the 20th International Joint Conference on Artificial Intelligence. pages 2670–2676.
9. Daoud Clarke. 2009. Context-theoretic semantics for natural language: an overview. In Proceedings of the Workshop on Geometrical Models of Natural Language Semantics. page 112119. [find](https://scholar.google.com/scholar?q=Daoud+Clarke%2E+2009%2E+Context%2Dtheoretic+semantics+for+natural+language%3A+an+overview%2E+In+Proceedings+of+the+Workshop+on+Geometrical+Models+of+Natural+Language+Semantics%2E+page+112119%2E)
10. Merley da Silva Conrado, Thiago Alexandre Salgueiro Pardo, and Solange Oliveira Rezende. 2013. A machine learning approach to automatic term extraction using a rich feature set. In Proceedings of the 2013 Conference of the North American Chapter of the Association of Computational Linguistics. pages… [find](https://scholar.google.com/scholar?q=Merley+da+Silva+Conrado%2C+Thiago+Alexandre+Salgueiro+Pardo%2C+and+Solange+Oliveira+Rezende%2E+2013%2E+A+machine+learning+approach+to+automatic+term+extraction+using+a+rich+feature+set%2E+In+Proceedings+of+the+)
11. Mohit Bansal, David Burkett, Gerard de Melo, and Dan Klein. 2014. [Structured learning for taxonomy induction with belief propagation](https://md.research.elamur.ai/free/en/papers/bansal-2014-structured-learning-taxonomy-induction-belief/). In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics. pages 1041–1051.
12. Jeroen de Knijff, Flavius Frasincar, and Frederik Hogenboom. 2013. Domain taxonomy learning from text: The subsumption method versus hierarchical clustering. Data Knowl. Eng. 83:54–69. [find](https://scholar.google.com/scholar?q=Jeroen+de+Knijff%2C+Flavius+Frasincar%2C+and+Frederik+Hogenboom%2E+2013%2E+Domain+taxonomy+learning+from+text%3A+The+subsumption+method+versus+hierarchical+clustering%2E+Data+Knowl%2E+Eng%2E+83%3A54%E2%80%9369%2E)
13. Marco Baroni, Raffaella Bernardi, Ngoc-Quynh Do, and Chung-chieh Shan. 2012. [Entailment above the word level in distributional semantics](https://md.research.elamur.ai/free/en/papers/baroni-2012-entailment-above-word-level-distributional/). In Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics. pages 23–32.
14. Omkar Deshpande, Digvijay S. Lamba, Michel Tourn, Sanjib Das, Sri Subramaniam, Anand Rajaraman, Venky Harinarayan, and AnHai Doan. 2013. Building, maintaining, and using knowledge bases: a report from the trenches. In Proceedings of the ACM SIGMOD International Conference on Management of Data. page… [find](https://scholar.google.com/scholar?q=Omkar+Deshpande%2C+Digvijay+S%2E+Lamba%2C+Michel+Tourn%2C+Sanjib+Das%2C+Sri+Subramaniam%2C+Anand+Rajaraman%2C+Venky+Harinarayan%2C+and+AnHai+Doan%2E+2013%2E+Building%2C+maintaining%2C+and+using+knowledge+bases%3A+a+report+from)
15. Marco Baroni and Alessandro Lenci. 2010. [Distributional memory: A general framework for corpus-based semantics](https://md.research.elamur.ai/free/en/papers/baroni-2010-distributional-memory-general-framework-corpus/). Computational Linguistics 36(4):673–721.
16. Marco Baroni and Alessandro Lenci. 2011. [How we blessed distributional semantic evaluation](https://md.research.elamur.ai/free/en/papers/baroni-2011-how-we-blessed-distributional-semantic/). In Proceedings of the GEMS 2011 Workshop on GEometrical Models of Natural Language Semantics. page 110.
17. Xin Dong, Evgeniy Gabrilovich, Geremy Heitz, Wilko Horn, Ni Lao, Kevin Murphy, Thomas Strohmann, Shaohua Sun, and Wei Zhang. 2014. Knowledge vault: a web-scale approach to probabilistic knowledge fusion. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data M… [find](https://scholar.google.com/scholar?q=Xin+Dong%2C+Evgeniy+Gabrilovich%2C+Geremy+Heitz%2C+Wilko+Horn%2C+Ni+Lao%2C+Kevin+Murphy%2C+Thomas+Strohmann%2C+Shaohua+Sun%2C+and+Wei+Zhang%2E+2014%2E+Knowledge+vault%3A+a+web%2Dscale+approach+to+probabilistic+knowledge+fusi)
18. Georgeta Bordea, Paul Buitelaar, Stefano Faralli, and Roberto Navigli. 2015. Semeval-2015 task 17: Taxonomy extraction evaluation (texeval). In Proceedings of the 9th International Workshop on Semantic Evaluation. pages 902–910. [find](https://scholar.google.com/scholar?q=Georgeta+Bordea%2C+Paul+Buitelaar%2C+Stefano+Faralli%2C+and+Roberto+Navigli%2E+2015%2E+Semeval%2D2015+task+17%3A+Taxonomy+extraction+evaluation+%28texeval%29%2E+In+Proceedings+of+the+9th+International+Workshop+on+Semanti)
19. Oren Etzioni, Michael J. Cafarella, Doug Downey, Stanley Kok, Ana-Maria Popescu, Tal Shaked, Stephen Soderland, Daniel S. Weld, and Alexander Yates. 2004. Web-scale information extraction in knowitall: (preliminary results). In Proceedings of the 13th international conference on World Wide Web. page… [find](https://scholar.google.com/scholar?q=Oren+Etzioni%2C+Michael+J%2E+Cafarella%2C+Doug+Downey%2C+Stanley+Kok%2C+Ana%2DMaria+Popescu%2C+Tal+Shaked%2C+Stephen+Soderland%2C+Daniel+S%2E+Weld%2C+and+Alexander+Yates%2E+2004%2E+Web%2Dscale+information+extraction+in+knowitall)
20. Georgeta Bordea, Els Lefever, and Paul Buitelaar. 2016. Semeval-2016 task 13: Taxonomy extraction evaluation (texeval-2). In Proceedings of the Stefan Evert. 2005. The statistics of word cooccurrences: word pairs and collocations. Ph.D. thesis, University of Stuttgart. [find](https://scholar.google.com/scholar?q=Georgeta+Bordea%2C+Els+Lefever%2C+and+Paul+Buitelaar%2E+2016%2E+Semeval%2D2016+task+13%3A+Taxonomy+extraction+evaluation+%28texeval%2D2%29%2E+In+Proceedings+of+the+Stefan+Evert%2E+2005%2E+The+statistics+of+word+cooccurrences)
21. Tiziano Flati, Daniele Vannella, Tommaso Pasini, and Roberto Navigli. 2014. Two is bigger (and better) than one: the wikipedia bitaxonomy project. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics. pages 945–955. [find](https://scholar.google.com/scholar?q=Tiziano+Flati%2C+Daniele+Vannella%2C+Tommaso+Pasini%2C+and+Roberto+Navigli%2E+2014%2E+Two+is+bigger+%28and+better%29+than+one%3A+the+wikipedia+bitaxonomy+project%2E+In+Proceedings+of+the+52nd+Annual+Meeting+of+the+Asso)
22. Tiziano Flati, Daniele Vannella, Tommaso Pasini, and Roberto Navigli. 2016. Multiwibi: The multilingual wikipedia bitaxonomy project. Artif. Intell. 241:66– 102. 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing. pages 95–105. [find](https://scholar.google.com/scholar?q=Tiziano+Flati%2C+Daniele+Vannella%2C+Tommaso+Pasini%2C+and+Roberto+Navigli%2E+2016%2E+Multiwibi%3A+The+multilingual+wikipedia+bitaxonomy+project%2E+Artif%2E+Intell%2E+241%3A66%E2%80%93+102%2E+7th+International+Joint+Conference+on+)
23. David Jurgens, Saif Mohammad, Peter D. Turney, and Keith J. Holyoak. 2012. [Semeval-2012 task 2: Measuring degrees of relational similarity](https://md.research.elamur.ai/free/en/papers/jurgens-2012-semeval-2012-task-2-measuring/). In Proceedings of the 6th International Workshop on Semantic Evaluation. pages 356–364.
24. Ruiji Fu, Jiang Guo, Bing Qin, Wanxiang Che, Haifeng Wang, and Ting Liu. 2014. [Learning semantic hierarchies via word embeddings](https://md.research.elamur.ai/free/en/papers/fu-2014-learning-semantic-hierarchies-word-embeddings/). In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics. pages 1199–1209.
25. Nobuhiro Kaji and Masaru Kitsuregawa. 2008. Using hidden markov random fields to combine distributional and pattern-based word clustering. In Proceedings of the 22nd International Conference on Computational Linguistics. pages 401–408. [find](https://scholar.google.com/scholar?q=Nobuhiro+Kaji+and+Masaru+Kitsuregawa%2E+2008%2E+Using+hidden+markov+random+fields+to+combine+distributional+and+pattern%2Dbased+word+clustering%2E+In+Proceedings+of+the+22nd+International+Conference+on+Comput)
26. Ruiji Fu, Bing Qin, and Ting Liu. 2013. Exploiting multiple sources for open-domain hypernym discovery. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing. pages 1224–1234. [find](https://scholar.google.com/scholar?q=Ruiji+Fu%2C+Bing+Qin%2C+and+Ting+Liu%2E+2013%2E+Exploiting+multiple+sources+for+open%2Ddomain+hypernym+discovery%2E+In+Proceedings+of+the+2013+Conference+on+Empirical+Methods+in+Natural+Language+Processing%2E+pages)
27. Richard M. Karp. 1971. A simple derivation of edmonds’ algorithm for optimum branchings. Networks 1(3):265–272. [find](https://scholar.google.com/scholar?q=Richard+M%2E+Karp%2E+1971%2E+A+simple+derivation+of+edmonds%E2%80%99+algorithm+for+optimum+branchings%2E+Networks+1%283%29%3A265%E2%80%93272%2E)
28. Maayan Geffet and Ido Dagan. 2005. [The distributional inclusion hypotheses and lexical entailment](https://md.research.elamur.ai/free/en/papers/geffet-2005-distributional-inclusion-hypotheses-lexical-entailment/). In Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics. pages 107–114.
29. Gregory Grefenstette. 2015. INRIASAC: simple hypernym extraction methods. In Proceedings of the 9th International Workshop on Semantic Evaluation. pages 911–914. [find](https://scholar.google.com/scholar?q=Gregory+Grefenstette%2E+2015%2E+INRIASAC%3A+simple+hypernym+extraction+methods%2E+In+Proceedings+of+the+9th+International+Workshop+on+Semantic+Evaluation%2E+pages+911%E2%80%93914%2E)
30. Amit Gupta, Francesco Piccinno, Mikhail Kozhevnikov, Marius Pasca, and Daniele Pighin. 2016. [Revisiting taxonomy induction over wikipedia](https://md.research.elamur.ai/free/en/papers/gupta-2016-revisiting-taxonomy-induction-over-wikipedia/). In Proceedings of the 26th International Conference on Computational Linguistics. pages 2300–2309.
31. Marti A. 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 International Conference on Computational Linguistics. pages 539–545.
32. Hans Hjelm and Paul Buitelaar. 2008. Multilingual evidence improves clustering-based taxonomy extraction. In Proceedings of the 18th European Conference on Artificial Intelligence. pages 288–292. [find](https://scholar.google.com/scholar?q=Hans+Hjelm+and+Paul+Buitelaar%2E+2008%2E+Multilingual+evidence+improves+clustering%2Dbased+taxonomy+extraction%2E+In+Proceedings+of+the+18th+European+Conference+on+Artificial+Intelligence%2E+pages+288%E2%80%93292%2E)
33. Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural Computation 9(8):1735–1780. [find](https://scholar.google.com/scholar?q=Sepp+Hochreiter+and+Ju%CC%88rgen+Schmidhuber%2E+1997%2E+Long+short%2Dterm+memory%2E+Neural+Computation+9%288%29%3A1735%E2%80%931780%2E)
34. Wen Hua, Zhongyuan Wang, Haixun Wang, Kai Zheng, and Xiaofang Zhou. 2017. Understand short texts by harvesting and analyzing semantic knowledge. IEEE Trans. Knowl. Data Eng. 29(3):499– 512. [find](https://scholar.google.com/scholar?q=Wen+Hua%2C+Zhongyuan+Wang%2C+Haixun+Wang%2C+Kai+Zheng%2C+and+Xiaofang+Zhou%2E+2017%2E+Understand+short+texts+by+harvesting+and+analyzing+semantic+knowledge%2E+IEEE+Trans%2E+Knowl%2E+Data+Eng%2E+29%283%29%3A499%E2%80%93+512%2E)
35. Ignacio Iacobacci, Mohammad Taher Pilehvar, and Roberto Navigli. 2015. [Sensembed: Learning sense embeddings for word and relational similarity](https://md.research.elamur.ai/free/en/papers/iacobacci-2015-sensembed-learning-sense-embeddings-word/). In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the Sebastian Köhler, Sandra C. Doelken, Christopher J. Mung…
36. Lili Kotlerman, Ido Dagan, Idan Szpektor, and Maayan Zhitomirsky-Geffet. 2010. Directional distributional similarity for lexical inference. Natural Language Engineering 16(4):359–389. [find](https://scholar.google.com/scholar?q=Lili+Kotlerman%2C+Ido+Dagan%2C+Idan+Szpektor%2C+and+Maayan+Zhitomirsky%2DGeffet%2E+2010%2E+Directional+distributional+similarity+for+lexical+inference%2E+Natural+Language+Engineering+16%284%29%3A359%E2%80%93389%2E)
37. Zornitsa Kozareva and Eduard H. Hovy. 2010. [A semi-supervised method to learn and construct taxonomies using the web](https://md.research.elamur.ai/free/en/papers/kozareva-2010-semi-supervised-method-learn-construct/). In Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing. pages 1110–1118.
38. Zornitsa Kozareva, Ellen Riloff, and Eduard H. 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 the 46th Annual Meeting of the Association for Computational Linguistics. pages 1048–1056.
39. Germán Kruszewski, Denis Paperno, and Marco Baroni. 2015. [Deriving boolean structures from distributional vectors](https://md.research.elamur.ai/free/en/papers/kruszewski-2015-deriving-boolean-structures-distributional-vectors/). TACL 3:375–388.
40. Alessandro Lenci and Giulia Benotto. 2012. [Identifying hypernyms in distributional semantic spaces](https://md.research.elamur.ai/free/en/papers/lenci-2012-identifying-hypernyms-distributional-semantic-spaces/). In Proceedings of the First Joint Conference on Lexical and Computational Semantics. pages 75–79.
41. Omer Levy, Ido Dagan, and Jacob Goldberger. 2014. Focused entailment graphs for open IE propositions. In Proceedings of the Eighteenth Conference on Computational Natural Language Learning. pages 87–97. [find](https://scholar.google.com/scholar?q=Omer+Levy%2C+Ido+Dagan%2C+and+Jacob+Goldberger%2E+2014%2E+Focused+entailment+graphs+for+open+IE+propositions%2E+In+Proceedings+of+the+Eighteenth+Conference+on+Computational+Natural+Language+Learning%2E+pages+87%E2%80%939)
42. Omer Levy, Steffen Remus, Chris Biemann, and Ido Dagan. 2015. [Do supervised distributional methods really learn lexical inference relations?](https://md.research.elamur.ai/free/en/papers/levy-2015-do-supervised-distributional-methods-really/) In Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. pages 970–97…
43. Anh Tuan Luu, Jung-jae Kim, and See-Kiong Ng. 2015. Incorporating trustiness and collective synonym/contrastive evidence into taxonomy construction. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. pages 1013–1022. [find](https://scholar.google.com/scholar?q=Anh+Tuan+Luu%2C+Jung%2Djae+Kim%2C+and+See%2DKiong+Ng%2E+2015%2E+Incorporating+trustiness+and+collective+synonym%2Fcontrastive+evidence+into+taxonomy+construction%2E+In+Proceedings+of+the+2015+Conference+on+Empirical+)
44. Anh Tuan Luu, Yi Tay, Siu Cheung Hui, and See-Kiong Ng. 2016b. Learning term embeddings for taxonomic relation identification using dynamic weighting neural network. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. pages 403–413. [find](https://scholar.google.com/scholar?q=Anh+Tuan+Luu%2C+Yi+Tay%2C+Siu+Cheung+Hui%2C+and+See%2DKiong+Ng%2E+2016b%2E+Learning+term+embeddings+for+taxonomic+relation+identification+using+dynamic+weighting+neural+network%2E+In+Proceedings+of+the+2016+Confere)
45. Farzaneh Mahdisoltani, Joanna Biega, and Fabian M. Suchanek. 2015. [YAGO3: A knowledge base from multilingual wikipedias](https://md.research.elamur.ai/free/en/papers/mahdisoltani-2015-yago3-knowledge-base-multilingual-wikipedias/). In Proceedings of the Seventh Biennial Conference on Innovative Data Systems Research.
46. Jinyang Li, Chengyu Wang, Xiaofeng He, Rong Zhang, and Ming Gao. 2015. User generated content oriented chinese taxonomy construction. In Proceedings of the 17th Asia-Pacific Web Conference. pages 623–634. [find](https://scholar.google.com/scholar?q=Jinyang+Li%2C+Chengyu+Wang%2C+Xiaofeng+He%2C+Rong+Zhang%2C+and+Ming+Gao%2E+2015%2E+User+generated+content+oriented+chinese+taxonomy+construction%2E+In+Proceedings+of+the+17th+Asia%2DPacific+Web+Conference%2E+pages+623%E2%80%93)
47. Kevin Meijer, Flavius Frasincar, and Frederik Hogenboom. 2014. A semantic approach for extracting domain taxonomies from text. Decision Support Systems 62:78–93. [find](https://scholar.google.com/scholar?q=Kevin+Meijer%2C+Flavius+Frasincar%2C+and+Frederik+Hogenboom%2E+2014%2E+A+semantic+approach+for+extracting+domain+taxonomies+from+text%2E+Decision+Support+Systems+62%3A78%E2%80%9393%2E)
48. Yuezhang Li, Ronghuo Zheng, Tian Tian, Zhiting Hu, Rahul Iyer, and Katia P. Sycara. 2016. Joint embedding of hierarchical categories and entities for concept categorization and dataless classification. In Proceedings of the 26th International Conference on Computational Linguistics. pages 2678–2688. [find](https://scholar.google.com/scholar?q=Yuezhang+Li%2C+Ronghuo+Zheng%2C+Tian+Tian%2C+Zhiting+Hu%2C+Rahul+Iyer%2C+and+Katia+P%2E+Sycara%2E+2016%2E+Joint+embedding+of+hierarchical+categories+and+entities+for+concept+categorization+and+dataless+classification)
49. Tomas Mikolov, Ilya Sutskever, Kai Chen, Gregory S. Corrado, and Jeffrey Dean. 2013. [Distributed representations of words and phrases and their compositionality](https://md.research.elamur.ai/free/en/papers/mikolov-2013-distributed-representations-words-phrases-their/). In Proceedings of the 27th Annual Conference on Neural Information Processing Systems. pages 3111–3119.
50. Jiaqing Liang, Yanghua Xiao, Yi Zhang, Seung-won Hwang, and Haixun Wang. 2017a. Graph-based wrong isa relation detection in a large-scale lexical taxonomy. In Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence. pages 1178– 1184. [find](https://scholar.google.com/scholar?q=Jiaqing+Liang%2C+Yanghua+Xiao%2C+Yi+Zhang%2C+Seung%2Dwon+Hwang%2C+and+Haixun+Wang%2E+2017a%2E+Graph%2Dbased+wrong+isa+relation+detection+in+a+large%2Dscale+lexical+taxonomy%2E+In+Proceedings+of+the+Thirty%2DFirst+AAAI+Conf)
51. Jiaqing Liang, Yi Zhang, Yanghua Xiao, Haixun Wang, Wei Wang, and Pinpin Zhu. 2017b. On the transitivity of hypernym-hyponym relations in data-driven lexical taxonomies. In Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence. pages 1185–1191. [find](https://scholar.google.com/scholar?q=Jiaqing+Liang%2C+Yi+Zhang%2C+Yanghua+Xiao%2C+Haixun+Wang%2C+Wei+Wang%2C+and+Pinpin+Zhu%2E+2017b%2E+On+the+transitivity+of+hypernym%2Dhyponym+relations+in+data%2Ddriven+lexical+taxonomies%2E+In+Proceedings+of+the+Thirty%2DF)
52. Dekang Lin. 1998. An information-theoretic definition of similarity. In Proceedings of the Fifteenth International Conference on Machine Learning. pages 296–304. [find](https://scholar.google.com/scholar?q=Dekang+Lin%2E+1998%2E+An+information%2Dtheoretic+definition+of+similarity%2E+In+Proceedings+of+the+Fifteenth+International+Conference+on+Machine+Learning%2E+pages+296%E2%80%93304%2E)
53. Jialu Liu, Jingbo Shang, Chi Wang, Xiang Ren, and Jiawei Han. 2015. Mining quality phrases from massive text corpora. In Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data. pages 1729–1744. [find](https://scholar.google.com/scholar?q=Jialu+Liu%2C+Jingbo+Shang%2C+Chi+Wang%2C+Xiang+Ren%2C+and+Jiawei+Han%2E+2015%2E+Mining+quality+phrases+from+massive+text+corpora%2E+In+Proceedings+of+the+2015+ACM+SIGMOD+International+Conference+on+Management+of+Da)
54. Anh Tuan Luu, Siu Cheung Hui, and See-Kiong Ng. 2016a. Utilizing temporal information for taxonomy construction. TACL 4:551–564. [find](https://scholar.google.com/scholar?q=Anh+Tuan+Luu%2C+Siu+Cheung+Hui%2C+and+See%2DKiong+Ng%2E+2016a%2E+Utilizing+temporal+information+for+taxonomy+construction%2E+TACL+4%3A551%E2%80%93564%2E)
55. Anh Tuan Luu, Jung-jae Kim, and See-Kiong Ng. 2014. Taxonomy construction using syntactic contextual evidence. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing. pages 810–819. [find](https://scholar.google.com/scholar?q=Anh+Tuan+Luu%2C+Jung%2Djae+Kim%2C+and+See%2DKiong+Ng%2E+2014%2E+Taxonomy+construction+using+syntactic+contextual+evidence%2E+In+Proceedings+of+the+2014+Conference+on+Empirical+Methods+in+Natural+Language+Processing)
56. George A. Miller. 1995. [Wordnet: A lexical database for english](https://md.research.elamur.ai/free/en/papers/miller-1994-wordnet/). Commun. ACM 38(11):39–41.
57. Shachar Mirkin, Ido Dagan, and Maayan Geffet. 2006. Integrating pattern-based and distributional similarity methods for lexical entailment acquisition. In Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Lingui… [find](https://scholar.google.com/scholar?q=Shachar+Mirkin%2C+Ido+Dagan%2C+and+Maayan+Geffet%2E+2006%2E+Integrating+pattern%2Dbased+and+distributional+similarity+methods+for+lexical+entailment+acquisition%2E+In+Proceedings+of+the+21st+International+Confere)
58. Paramita Mirza and Sara Tonelli. 2016. On the contribution of word embeddings to temporal relation classification. In Proceedings of the 26th International Conference on Computational Linguistics. pages 2818–2828. [find](https://scholar.google.com/scholar?q=Paramita+Mirza+and+Sara+Tonelli%2E+2016%2E+On+the+contribution+of+word+embeddings+to+temporal+relation+classification%2E+In+Proceedings+of+the+26th+International+Conference+on+Computational+Linguistics%2E+pag)
59. Andriy Mnih and Koray Kavukcuoglu. 2013. Learning word embeddings efficiently with noise-contrastive estimation. In Proceedings of the 27th Annual Conference on Neural Information Processing Systems. pages 2265–2273. [find](https://scholar.google.com/scholar?q=Andriy+Mnih+and+Koray+Kavukcuoglu%2E+2013%2E+Learning+word+embeddings+efficiently+with+noise%2Dcontrastive+estimation%2E+In+Proceedings+of+the+27th+Annual+Conference+on+Neural+Information+Processing+Systems%2E+)
60. Ndapandula Nakashole, Gerhard Weikum, and Fabian M. Suchanek. 2012. [PATTY: A taxonomy of relational patterns with semantic types](https://md.research.elamur.ai/free/en/papers/nakashole-2012-patty-taxonomy-relational-patterns-semantic/). In Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning. pages 1135–1145.
61. Roberto Navigli and Simone Paolo Ponzetto. 2012. Babelnet: The automatic construction, evaluation and application of a wide-coverage multilingual semantic network. Artif. Intell. 193:217–250. [find](https://scholar.google.com/scholar?q=Roberto+Navigli+and+Simone+Paolo+Ponzetto%2E+2012%2E+Babelnet%3A+The+automatic+construction%2C+evaluation+and+application+of+a+wide%2Dcoverage+multilingual+semantic+network%2E+Artif%2E+Intell%2E+193%3A217%E2%80%93250%2E)
62. Roberto Navigli and Paola Velardi. 2004. Learning domain ontologies from document warehouses and dedicated web sites. Computational Linguistics 30(2):151–179. [find](https://scholar.google.com/scholar?q=Roberto+Navigli+and+Paola+Velardi%2E+2004%2E+Learning+domain+ontologies+from+document+warehouses+and+dedicated+web+sites%2E+Computational+Linguistics+30%282%29%3A151%E2%80%93179%2E)
63. Roberto Navigli and Paola Velardi. 2010. [Learning word-class lattices for definition and hypernym extraction](https://md.research.elamur.ai/free/en/papers/navigli-2010-learning-word-class-lattices-definition/). In Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics. pages 1318–1327.
64. Roberto Navigli, Paola Velardi, and Stefano Faralli. 2011. [A graph-based algorithm for inducing lexical taxonomies from scratch](https://md.research.elamur.ai/free/en/papers/navigli-2011-graph-based-algorithm-inducing-lexical/). In Proceedings of the 22nd International Joint Conference on Artificial Intelligence. pages 1872–1877.
65. Robert C. Nickerson, Upkar Varshney, and Jan Muntermann. 2013. A method for taxonomy development and its application in information systems. EJIS 22(3):336–359. [find](https://scholar.google.com/scholar?q=Robert+C%2E+Nickerson%2C+Upkar+Varshney%2C+and+Jan+Muntermann%2E+2013%2E+A+method+for+taxonomy+development+and+its+application+in+information+systems%2E+EJIS+22%283%29%3A336%E2%80%93359%2E)
66. Sebastian Padó and Mirella Lapata. 2003. Constructing semantic space models from parsed corpora. In Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics. pages 128– 135. [find](https://scholar.google.com/scholar?q=Sebastian+Pado%CC%81+and+Mirella+Lapata%2E+2003%2E+Constructing+semantic+space+models+from+parsed+corpora%2E+In+Proceedings+of+the+41st+Annual+Meeting+of+the+Association+for+Computational+Linguistics%2E+pages+128%E2%80%93)
67. Alexander Panchenko, Stefano Faralli, Eugen Ruppert, Steffen Remus, Hubert Naets, Cédrick Fairon, Simone Paolo Ponzetto, and Chris Biemann. 2016. [TAXI at semeval-2016 task 13: a taxonomy induction method based on lexico-syntactic patterns, substrings and focused crawling](https://md.research.elamur.ai/free/en/papers/panchenko-2016-taxi-semeval-2016-task-13/). In Proceedings of the 10th…
68. Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014. [Glove: Global vectors for word representation](https://md.research.elamur.ai/free/en/papers/pennington-2014-glove-global-vectors-word-representation/). In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing. pages 1532–1543.
69. Simone Paolo Ponzetto and Roberto Navigli. 2009. Large-scale taxonomy mapping for restructuring and integrating wikipedia. In Proceedings of the 21st International Joint Conference on Artificial Intelligence. pages 2083–2088. [find](https://scholar.google.com/scholar?q=Simone+Paolo+Ponzetto+and+Roberto+Navigli%2E+2009%2E+Large%2Dscale+taxonomy+mapping+for+restructuring+and+integrating+wikipedia%2E+In+Proceedings+of+the+21st+International+Joint+Conference+on+Artificial+Intel)
70. Muhammad Atif Qureshi, Colm O’Riordan, and Gabriella Pasi. 2012. Short-text domain specific key terms/phrases extraction using an n-gram model with wikipedia. In Proceedings of the 21st ACM International Conference on Information and Knowledge Management. pages 2515–2518. [find](https://scholar.google.com/scholar?q=Muhammad+Atif+Qureshi%2C+Colm+O%E2%80%99Riordan%2C+and+Gabriella+Pasi%2E+2012%2E+Short%2Dtext+domain+specific+key+terms%2Fphrases+extraction+using+an+n%2Dgram+model+with+wikipedia%2E+In+Proceedings+of+the+21st+ACM+Internatio)
71. Marek Rei and Ted Briscoe. 2014. Looking for hyponyms in vector space. In Proceedings of the Eighteenth Conference on Computational Natural Language Learning. pages 68–77. [find](https://scholar.google.com/scholar?q=Marek+Rei+and+Ted+Briscoe%2E+2014%2E+Looking+for+hyponyms+in+vector+space%2E+In+Proceedings+of+the+Eighteenth+Conference+on+Computational+Natural+Language+Learning%2E+pages+68%E2%80%9377%2E)
72. Laura Rimell. 2014. Distributional lexical entailment by topic coherence. In Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics. pages 511–519. [find](https://scholar.google.com/scholar?q=Laura+Rimell%2E+2014%2E+Distributional+lexical+entailment+by+topic+coherence%2E+In+Proceedings+of+the+14th+Conference+of+the+European+Chapter+of+the+Association+for+Computational+Linguistics%2E+pages+511%E2%80%93519%2E)
73. Alan Ritter, Stephen Soderland, and Oren Etzioni. 2009. What is this, anyway: Automatic hypernym discovery. In Learning by Reading and Learning to Read, Proceedings of the 2009 AAAI Spring Symposium. pages 88–93. [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+Learning+by+Reading+and+Learning+to+Read%2C+Proceedings+of+the+2009+AAAI+Spring+Symposium%2E+)
74. Stephen Roller and Katrin Erk. 2016. Relations such as hypernymy: Identifying and exploiting hearst patterns in distributional vectors for lexical entailment. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. pages 2163–2172. [find](https://scholar.google.com/scholar?q=Stephen+Roller+and+Katrin+Erk%2E+2016%2E+Relations+such+as+hypernymy%3A+Identifying+and+exploiting+hearst+patterns+in+distributional+vectors+for+lexical+entailment%2E+In+Proceedings+of+the+2016+Conference+on+)
75. Stephen Roller, Katrin Erk, and Gemma Boleda. 2014. [Inclusive yet selective: Supervised distributional hypernymy detection](https://md.research.elamur.ai/free/en/papers/roller-2014-inclusive-yet-selective-supervised-distributional/). In Proceedings of the 25th International Conference on Computational Linguistics. pages 1025–1036.
76. Enrico Santus, Alessandro Lenci, Qin Lu, and Sabine Schulte im Walde. 2014. Chasing hypernyms in vector spaces with entropy. In Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics. pages 38–42. [find](https://scholar.google.com/scholar?q=Enrico+Santus%2C+Alessandro+Lenci%2C+Qin+Lu%2C+and+Sabine+Schulte+im+Walde%2E+2014%2E+Chasing+hypernyms+in+vector+spaces+with+entropy%2E+In+Proceedings+of+the+14th+Conference+of+the+European+Chapter+of+the+Associ)
77. Hinrich Schütze. 1993. Part-of-speech induction from scratch. In Proceedings of the 31st Annual Meeting of the Association for Computational Linguistics. pages 251–258. [find](https://scholar.google.com/scholar?q=Hinrich+Schu%CC%88tze%2E+1993%2E+Part%2Dof%2Dspeech+induction+from+scratch%2E+In+Proceedings+of+the+31st+Annual+Meeting+of+the+Association+for+Computational+Linguistics%2E+pages+251%E2%80%93258%2E)
78. Wei Shen, Jianyong Wang, and Jiawei Han. 2015. Entity linking with a knowledge base: Issues, techniques, and solutions. IEEE Trans. Knowl. Data Eng. 27(2):443–460. [find](https://scholar.google.com/scholar?q=Wei+Shen%2C+Jianyong+Wang%2C+and+Jiawei+Han%2E+2015%2E+Entity+linking+with+a+knowledge+base%3A+Issues%2C+techniques%2C+and+solutions%2E+IEEE+Trans%2E+Knowl%2E+Data+Eng%2E+27%282%29%3A443%E2%80%93460%2E)
79. Wei Shen, Jianyong Wang, Ping Luo, and Min Wang. 2012. A graph-based approach for ontology population with named entities. In Proceedings of the 21st ACM International Conference on Information and Knowledge Management. pages 345–354. [find](https://scholar.google.com/scholar?q=Wei+Shen%2C+Jianyong+Wang%2C+Ping+Luo%2C+and+Min+Wang%2E+2012%2E+A+graph%2Dbased+approach+for+ontology+population+with+named+entities%2E+In+Proceedings+of+the+21st+ACM+International+Conference+on+Information+and+Kn)
80. Vered Shwartz and Ido Dagan. 2016. The roles of path-based and distributional information in recognizing lexical semantic relations. CoRR abs/1608.05014. — [arXiv:1608.05014](https://arxiv.org/abs/1608.05014) [find](https://scholar.google.com/scholar?q=Vered+Shwartz+and+Ido+Dagan%2E+2016%2E+The+roles+of+path%2Dbased+and+distributional+information+in+recognizing+lexical+semantic+relations%2E+CoRR+abs%2F1608%2E05014%2E+%E2%80%94+%5BarXiv%3A1608%2E05014%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F160)
81. Vered Shwartz, Yoav Goldberg, and Ido Dagan. 2016. [Improving hypernymy detection with an integrated path-based and distributional method](https://md.research.elamur.ai/free/en/papers/shwartz-2016-improving-hypernymy-detection-integrated-path/). In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics.
82. Vered Shwartz, Enrico Santus, and Dominik Schlechtweg. 2017. [Hypernyms under siege: Linguistically-motivated artillery for hypernymy detection](https://md.research.elamur.ai/free/en/papers/shwartz-2017-hypernyms-under-siege-linguistically-motivated/). In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics. page 6575.
83. Rion Snow, Daniel Jurafsky, and Andrew Y. Ng. 2004. Learning syntactic patterns for automatic hypernym discovery. In Proceedings of the 17th Annual Conference on Neural Information Processing Systems. pages 1297–1304. [find](https://scholar.google.com/scholar?q=Rion+Snow%2C+Daniel+Jurafsky%2C+and+Andrew+Y%2E+Ng%2E+2004%2E+Learning+syntactic+patterns+for+automatic+hypernym+discovery%2E+In+Proceedings+of+the+17th+Annual+Conference+on+Neural+Information+Processing+Systems%2E)
84. Rion Snow, Daniel Jurafsky, and Andrew Y. Ng. 2006. Semantic taxonomy induction from heterogenous evidence. In Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference. [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+the+21st+International+Conference+on+Computational+Linguistics+and+44th+An)
85. Yangqiu Song, Shixia Liu, Xueqing Liu, and Haixun Wang. 2015. Automatic taxonomy construction from keywords via scalable bayesian rose trees. IEEE Trans. Knowl. Data Eng. 27(7):1861–1874. [find](https://scholar.google.com/scholar?q=Yangqiu+Song%2C+Shixia+Liu%2C+Xueqing+Liu%2C+and+Haixun+Wang%2E+2015%2E+Automatic+taxonomy+construction+from+keywords+via+scalable+bayesian+rose+trees%2E+IEEE+Trans%2E+Knowl%2E+Data+Eng%2E+27%287%29%3A1861%E2%80%931874%2E)
86. Fabian M. Suchanek, Gjergji Kasneci, and Gerhard Weikum. 2007. Yago: a core of semantic knowledge. In 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+Proceedings+of+the+16th+International+Conference+on+World+Wide+Web%2E+pages+697%E2%80%93706%2E)
87. Liling Tan, Rohit Gupta, and Josef van Genabith. 2015. USAAR-WLV: hypernym generation with deep neural nets. In Proceedings of the 9th International Workshop on Semantic Evaluation. pages 932–937. [find](https://scholar.google.com/scholar?q=Liling+Tan%2C+Rohit+Gupta%2C+and+Josef+van+Genabith%2E+2015%2E+USAAR%2DWLV%3A+hypernym+generation+with+deep+neural+nets%2E+In+Proceedings+of+the+9th+International+Workshop+on+Semantic+Evaluation%2E+pages+932%E2%80%93937%2E)
88. Peter D. Turney and Saif M. Mohammad. 2015. Experiments with three approaches to recognizing lexical entailment. Natural Language Engineering 21(3):437–476. [find](https://scholar.google.com/scholar?q=Peter+D%2E+Turney+and+Saif+M%2E+Mohammad%2E+2015%2E+Experiments+with+three+approaches+to+recognizing+lexical+entailment%2E+Natural+Language+Engineering+21%283%29%3A437%E2%80%93476%2E)
89. Peter D. Turney and Patrick Pantel. 2010. [From frequency to meaning: Vector space models of semantics](https://md.research.elamur.ai/free/en/papers/turney-2010-frequency-meaning-vector-space-models/). J. Artif. Intell. Res. (JAIR) 37:141–188.
90. Dmitry Ustalov, Nikolay Arefyev, Chris Biemann, and Alexander Panchenko. 2017. Negative sampling improves hypernymy extraction based on projection learning. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics. page 543550. [find](https://scholar.google.com/scholar?q=Dmitry+Ustalov%2C+Nikolay+Arefyev%2C+Chris+Biemann%2C+and+Alexander+Panchenko%2E+2017%2E+Negative+sampling+improves+hypernymy+extraction+based+on+projection+learning%2E+In+Proceedings+of+the+15th+Conference+of+th)
91. Paola Velardi, Stefano Faralli, and Roberto Navigli. 2013. [Ontolearn reloaded: A graph-based algorithm for taxonomy induction](https://md.research.elamur.ai/free/en/papers/velardi-2013-ontolearn-reloaded-graph-based-algorithm/). Computational Linguistics 39(3):665–707.
92. Chengyu Wang, Ming Gao, Xiaofeng He, and Rong Zhang. 2015. Challenges in chinese knowledge graph construction. In Proceedings of the 31st IEEE International Conference on Data Engineering Workshops. pages 59–61. [find](https://scholar.google.com/scholar?q=Chengyu+Wang%2C+Ming+Gao%2C+Xiaofeng+He%2C+and+Rong+Zhang%2E+2015%2E+Challenges+in+chinese+knowledge+graph+construction%2E+In+Proceedings+of+the+31st+IEEE+International+Conference+on+Data+Engineering+Workshops%2E+p)
93. Chengyu Wang and Xiaofeng He. 2016. Chinese hypernym-hyponym extraction from user generated categories. In Proceedings of the 26th International Conference on Computational Linguistics. pages 1350–1361. [find](https://scholar.google.com/scholar?q=Chengyu+Wang+and+Xiaofeng+He%2E+2016%2E+Chinese+hypernym%2Dhyponym+extraction+from+user+generated+categories%2E+In+Proceedings+of+the+26th+International+Conference+on+Computational+Linguistics%2E+pages+1350%E2%80%93136)
94. Chengyu Wang, Junchi Yan, Aoying Zhou, and Xiaofeng He. 2017. Transductive non-linear learning for chinese hypernym prediction. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Chengyu+Wang%2C+Junchi+Yan%2C+Aoying+Zhou%2C+and+Xiaofeng+He%2E+2017%2E+Transductive+non%2Dlinear+learning+for+chinese+hypernym+prediction%2E+In+Proceedings+of+the+55th+Annual+Meeting+of+the+Association+for+Computa)
95. Julie Weeds, Daoud Clarke, Jeremy Reffin, David J. Weir, and Bill Keller. 2014. [Learning to distinguish hypernyms and co-hyponyms](https://md.research.elamur.ai/free/en/papers/weeds-2014-learning-distinguish-hypernyms-co-hyponyms/). In Proceedings of the 25th International Conference on Computational Linguistics. pages 2249–2259.
96. Julie Weeds, David J. Weir, and Diana McCarthy. 2004. Characterising measures of lexical distributional similarity. In Proceedings of the 20th International Conference on Computational Linguistics. [find](https://scholar.google.com/scholar?q=Julie+Weeds%2C+David+J%2E+Weir%2C+and+Diana+McCarthy%2E+2004%2E+Characterising+measures+of+lexical+distributional+similarity%2E+In+Proceedings+of+the+20th+International+Conference+on+Computational+Linguistics%2E)
97. Bifan Wei, Jun Liu, Jian Ma, Qinghua Zheng, Wei Zhang, and Boqin Feng. 2014. Motif-based hyponym relation extraction from wikipedia hyperlinks. IEEE Trans. Knowl. Data Eng. 26(10):2507– 2519. [find](https://scholar.google.com/scholar?q=Bifan+Wei%2C+Jun+Liu%2C+Jian+Ma%2C+Qinghua+Zheng%2C+Wei+Zhang%2C+and+Boqin+Feng%2E+2014%2E+Motif%2Dbased+hyponym+relation+extraction+from+wikipedia+hyperlinks%2E+IEEE+Trans%2E+Knowl%2E+Data+Eng%2E+26%2810%29%3A2507%E2%80%93+2519%2E)
98. Wentao Wu, Hongsong Li, Haixun Wang, and Kenny Qili Zhu. 2012. Probase: a probabilistic taxonomy for text understanding. In Proceedings of the ACM SIGMOD International Conference on Management of Data. pages 481–492. [find](https://scholar.google.com/scholar?q=Wentao+Wu%2C+Hongsong+Li%2C+Haixun+Wang%2C+and+Kenny+Qili+Zhu%2E+2012%2E+Probase%3A+a+probabilistic+taxonomy+for+text+understanding%2E+In+Proceedings+of+the+ACM+SIGMOD+International+Conference+on+Management+of+Data)
99. Ichiro Yamada, Kentaro Torisawa, Jun’ichi Kazama, Kow Kuroda, Masaki Murata, Stijn De Saeger, Francis Bond, and Asuka Sumida. 2009. Hypernym discovery based on distributional similarity and hierarchical structures. In Proceedings of the 2009 Conference on Empirical Methods in Natural Language Proces… [find](https://scholar.google.com/scholar?q=Ichiro+Yamada%2C+Kentaro+Torisawa%2C+Jun%E2%80%99ichi+Kazama%2C+Kow+Kuroda%2C+Masaki+Murata%2C+Stijn+De+Saeger%2C+Francis+Bond%2C+and+Asuka+Sumida%2E+2009%2E+Hypernym+discovery+based+on+distributional+similarity+and+hierarchic)
100. Josuke Yamane, Tomoya Takatani, Hitoshi Yamada, Makoto Miwa, and Yutaka Sasaki. 2016. Distributional hypernym generation by jointly learning clusters and projections. In Proceedings of the 26th International Conference on Computational Linguistics. pages 1871–1879. [find](https://scholar.google.com/scholar?q=Josuke+Yamane%2C+Tomoya+Takatani%2C+Hitoshi+Yamada%2C+Makoto+Miwa%2C+and+Yutaka+Sasaki%2E+2016%2E+Distributional+hypernym+generation+by+jointly+learning+clusters+and+projections%2E+In+Proceedings+of+the+26th+Intern)
101. Hui Yang. 2012. Constructing task-specific taxonomies for document collection browsing. In Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning. pages 1278–1289. [find](https://scholar.google.com/scholar?q=Hui+Yang%2E+2012%2E+Constructing+task%2Dspecific+taxonomies+for+document+collection+browsing%2E+In+Proceedings+of+the+2012+Joint+Conference+on+Empirical+Methods+in+Natural+Language+Processing+and+Computationa)
102. Shuo Yang, Lei Zou, Zhongyuan Wang, Jun Yan, and Ji-Rong Wen. 2017. Efficiently answering technical questions - A knowledge graph approach. In Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence. pages 3111–3118. [find](https://scholar.google.com/scholar?q=Shuo+Yang%2C+Lei+Zou%2C+Zhongyuan+Wang%2C+Jun+Yan%2C+and+Ji%2DRong+Wen%2E+2017%2E+Efficiently+answering+technical+questions+%2D+A+knowledge+graph+approach%2E+In+Proceedings+of+the+Thirty%2DFirst+AAAI+Conference+on+Artifi)
103. Zheng Yu, Haixun Wang, Xuemin Lin, and Min Wang. 2015. Learning term embeddings for hypernymy identification. In Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence. pages 1390–1397. [find](https://scholar.google.com/scholar?q=Zheng+Yu%2C+Haixun+Wang%2C+Xuemin+Lin%2C+and+Min+Wang%2E+2015%2E+Learning+term+embeddings+for+hypernymy+identification%2E+In+Proceedings+of+the+Twenty%2DFourth+International+Joint+Conference+on+Artificial+Intellige)
104. Yuchen Zhang, Amr Ahmed, Vanja Josifovski, and Alexander J. Smola. 2014. Taxonomy discovery for personalized recommendation. In Proceedings of the Seventh ACM International Conference on Web Search and Data Mining. pages 243–252. [find](https://scholar.google.com/scholar?q=Yuchen+Zhang%2C+Amr+Ahmed%2C+Vanja+Josifovski%2C+and+Alexander+J%2E+Smola%2E+2014%2E+Taxonomy+discovery+for+personalized+recommendation%2E+In+Proceedings+of+the+Seventh+ACM+International+Conference+on+Web+Search+an)
105. Maayan Zhitomirsky-Geffet and Ido Dagan. 2009. [Bootstrapping distributional feature vector quality](https://md.research.elamur.ai/free/en/papers/geffet-2009-bootstrapping-distributional-feature-vector-quality/). Computational Linguistics 35(3):435–461.
106. Xingwei Zhu, Zhaoyan Ming, Xiaoyan Zhu, and Tat-Seng Chua. 2013. Topic hierarchy construction for the organization of multi-source user generated contents. In Proceedings of the 36th International ACM SIGIR conference on research and development in Information Retrieval. pages 233–242. [find](https://scholar.google.com/scholar?q=Xingwei+Zhu%2C+Zhaoyan+Ming%2C+Xiaoyan+Zhu%2C+and+Tat%2DSeng+Chua%2E+2013%2E+Topic+hierarchy+construction+for+the+organization+of+multi%2Dsource+user+generated+contents%2E+In+Proceedings+of+the+36th+International+ACM)

*Updated 3 November 2026*