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{{Short description|Subtask of information extraction}}
{{technical|date=December 2018}}
{{technical|date=December 2018}}
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In the [[semantic web]] era, a growing number of communities and networked enterprises started to access and interoperate through the [[internet]]. Modeling these communities and their information needs is important for several [[web application]]s, like topic-driven [[web crawler]]s,<ref>Menczer F., Pant G. and Srinivasan P. [http://citeseer.ist.psu.edu/menczer02topicdriven.html Topic-Driven Crawlers: machine learning issues].</ref> [[web service]]s,<ref>Fan J. and Kambhampati S. [http://portal.acm.org/citation.cfm?id=1058150.1058156 A Snapshot of Public Web Services], in ACM SIGMOD Record archive Volume 34 , Issue 1 (March 2005).</ref> [[Recommendation system|recommender systems]],<ref>Yan Zheng Wei, Luc Moreau, Nicholas R. Jennings. [http://portal.acm.org/citation.cfm?id=1080344&dl=ACM&coll=&CFID=15151515&CFTOKEN=6184618 A market-based approach to recommender systems], in ACM Transactions on Information Systems (TOIS), 23(3), 2005.</ref> etc. The development of terminology extraction is also essential to the [[language industry]].
In the [[semantic web]] era, a growing number of communities and networked enterprises started to access and interoperate through the [[internet]]. Modeling these communities and their information needs is important for several [[web application]]s, like topic-driven [[web crawler]]s,<ref>Menczer F., Pant G. and Srinivasan P. [http://citeseer.ist.psu.edu/menczer02topicdriven.html Topic-Driven Crawlers: machine learning issues].</ref> [[web service]]s,<ref>Fan J. and Kambhampati S. [http://portal.acm.org/citation.cfm?id=1058150.1058156 A Snapshot of Public Web Services], in ACM SIGMOD Record archive Volume 34 , Issue 1 (March 2005).</ref> [[Recommendation system|recommender systems]],<ref>Yan Zheng Wei, Luc Moreau, Nicholas R. Jennings. [http://portal.acm.org/citation.cfm?id=1080344&dl=ACM&coll=&CFID=15151515&CFTOKEN=6184618 A market-based approach to recommender systems], in ACM Transactions on Information Systems (TOIS), 23(3), 2005.</ref> etc. The development of terminology extraction is also essential to the [[language industry]].


One of the first steps to model the [[Domain knowledge|knowledge domain]] of a [[virtual community]] is to collect a vocabulary of domain-relevant terms, constituting the linguistic surface manifestation of domain [[Ontology (computer science)#Classes .28concepts.29|concepts]]. Several methods to automatically extract technical terms from domain-specific document warehouses have been described in the literature.<ref>Bourigault D. and Jacquemin C. [http://acl.ldc.upenn.edu/E/E99/E99-1003.pdf Term Extraction+Term Clustering: an integrated platform for computer-aided terminology] {{Webarchive|url=https://web.archive.org/web/20060619123604/http://acl.ldc.upenn.edu/E/E99/E99-1003.pdf |date=2006-06-19 }}, in Proc. of EACL, 1999.</ref><ref>{{cite journal|last1=Collier|first1=N.|last2=Nobata|first2=C.|last3=Tsujii|first3=J.|title=Automatic acquisition and classification of terminology using a tagged corpus in the molecular biology domain|journal=Terminology|volume=7|issue=2|pages=239–257|year=2002|doi=10.1075/term.7.2.07col}}</ref><ref>K. Frantzi, S. Ananiadou and H. Mima. (2000). [http://arnetminer.org/viewpub.do?pid=986374 Automatic recognition of multi-word terms: the C-value/NC-value method.] In: C. Nikolau and C. Stephanidis (Eds.) International Journal on Digital Libraries, Vol. 3, No. 2., pp. 115-130.</ref><ref>K. Frantzi, S. Ananiadou and J. Tsujii. (1998) [http://dl.acm.org/citation.cfm?id=696825 The C-value/NC-value Method of Automatic Recognition of Multi-word Terms], In: ECDL '98 Proceedings of the Second European Conference on Research and Advanced Technology for Digital Libraries, pp. 585-604. {{ISBN|3-540-65101-2}}</ref><ref>{{cite journal|author1=L. Kozakov |author2=Y. Park |author3=T. Fin |author4=Y. Drissi |author5=Y. Doganata |author6=T. Cofino. |last-author-amp=yes |url=http://www.research.ibm.com/people/y/yurdaer/papers/ibmsysjournal2004a.pdf|title=Glossary extraction and utilization in the information search and delivery system for IBM Technical Support|journal=IBM Systems Journal|volume=43|issue=3|year=2004}}</ref><ref>Navigli R. and Velardi, P. [http://portal.acm.org/citation.cfm?id=1105712 Learning Domain Ontologies from Document Warehouses and Dedicated Web Sites]. Computational Linguistics. 30 (2), MIT Press, 2004, pp. 151-179</ref><ref>Oliver, A. and Vàzquez, M. [http://aclweb.org/anthology/R15-1062 TBXTools: A Free, Fast and Flexible Tool for Automatic Terminology Extraction]. Proceedings of Recent Advances in Natural Language Processing (RANLP 2015), 2015, pp. 473–479</ref><ref>Y. Park, R. J. Byrd, B. Boguraev. [http://portal.acm.org/citation.cfm?id=1072370&dl=ACM&coll=&CFID=15151515&CFTOKEN=6184618 "Automatic glossary extraction: beyond terminology identification"], International Conference On Computational Linguistics, Proceedings of the 19th international conference on Computational linguistics - Taipei, Taiwan, 2002.</ref><ref>[https://web.archive.org/web/20070106074102/http://lcl2.di.uniroma1.it/~sclano/ Sclano, F.] and [http://www.dsi.uniroma1.it/~velardi/welcome.htm Velardi, P.]. [https://web.archive.org/web/20070214092612/http://lcl2.di.uniroma1.it/termextractor/ TermExtractor]: a Web Application to Learn the Shared Terminology of Emergent Web Communities. To appear in Proc. of the 3rd International Conference on Interoperability for Enterprise Software and Applications (I-ESA 2007). Funchal (Madeira Island), Portugal, March 28–30th, 2007.</ref><ref>P. Velardi, R. Navigli, P. D'Amadio. [http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=4629722 Mining the Web to Create Specialized Glossaries], IEEE Intelligent Systems, 23(5), IEEE Press, 2008, pp. 18-25.</ref><ref>Wermter J. and Hahn U. [http://portal.acm.org/citation.cfm?id=1088648 Finding New terminology in Very large Corpora], in Proc. of K-CAP'05, October 2–5, 2005, Banff, Alberta, Canada</ref><ref>Wong, W., Liu, W. & Bennamoun, M. (2007) [http://portal.acm.org/citation.cfm?id=1378245.1378253 Determining Termhood for Learning Domain Ontologies using Domain Prevalence and Tendency]. In: 6th Australasian Conference on Data Mining (AusDM); Gold Coast. {{ISBN|978-1-920682-51-4}}</ref><ref>Wong, W., Liu, W. & Bennamoun, M. (2007) [http://portal.acm.org/citation.cfm?id=1378254&jmp=cit&coll=GUIDE&dl=GUIDE Determining Termhood for Learning Domain Ontologies in a Probabilistic Framework]. In: 6th Australasian Conference on Data Mining (AusDM); Gold Coast. {{ISBN|978-1-920682-51-4}}</ref>
One of the first steps to model a [[Domain knowledge|knowledge domain]] is to collect a vocabulary of domain-relevant terms, constituting the linguistic surface manifestation of domain [[Ontology (computer science)#Classes .28concepts.29|concepts]]. Several methods to automatically extract technical terms from domain-specific document warehouses have been described in the literature.<ref>Bourigault D. and Jacquemin C. [http://acl.ldc.upenn.edu/E/E99/E99-1003.pdf Term Extraction+Term Clustering: an integrated platform for computer-aided terminology] {{Webarchive|url=https://web.archive.org/web/20060619123604/http://acl.ldc.upenn.edu/E/E99/E99-1003.pdf |date=2006-06-19 }}, in Proc. of EACL, 1999.</ref><ref>{{cite journal|last1=Collier|first1=N.|last2=Nobata|first2=C.|last3=Tsujii|first3=J.|title=Automatic acquisition and classification of terminology using a tagged corpus in the molecular biology domain|journal=Terminology|volume=7|issue=2|pages=239–257|year=2002|doi=10.1075/term.7.2.07col}}</ref><ref>K. Frantzi, S. Ananiadou and H. Mima. (2000). [http://arnetminer.org/viewpub.do?pid=986374 Automatic recognition of multi-word terms: the C-value/NC-value method.] In: C. Nikolau and C. Stephanidis (Eds.) International Journal on Digital Libraries, Vol. 3, No. 2., pp. 115-130.</ref><ref>K. Frantzi, S. Ananiadou and J. Tsujii. (1998) [http://dl.acm.org/citation.cfm?id=696825 The C-value/NC-value Method of Automatic Recognition of Multi-word Terms], In: ECDL '98 Proceedings of the Second European Conference on Research and Advanced Technology for Digital Libraries, pp. 585-604. {{ISBN|3-540-65101-2}}</ref><ref>{{cite journal|author1=L. Kozakov |author2=Y. Park |author3=T. Fin |author4=Y. Drissi |author5=Y. Doganata |author6=T. Cofino. |name-list-style=amp |url=http://www.research.ibm.com/people/y/yurdaer/papers/ibmsysjournal2004a.pdf|title=Glossary extraction and utilization in the information search and delivery system for IBM Technical Support|journal=IBM Systems Journal|volume=43|issue=3|pages=546–563 |year=2004|doi=10.1147/sj.433.0546 }}</ref><ref>Navigli R. and Velardi, P. [http://portal.acm.org/citation.cfm?id=1105712 Learning Domain Ontologies from Document Warehouses and Dedicated Web Sites]. Computational Linguistics. 30 (2), MIT Press, 2004, pp. 151-179</ref><ref>Oliver, A. and Vàzquez, M. [http://aclweb.org/anthology/R15-1062 TBXTools: A Free, Fast and Flexible Tool for Automatic Terminology Extraction]. Proceedings of Recent Advances in Natural Language Processing (RANLP 2015), 2015, pp. 473–479</ref><ref>Y. Park, R. J. Byrd, B. Boguraev. [http://portal.acm.org/citation.cfm?id=1072370&dl=ACM&coll=&CFID=15151515&CFTOKEN=6184618 "Automatic glossary extraction: beyond terminology identification"], International Conference On Computational Linguistics, Proceedings of the 19th international conference on Computational linguistics - Taipei, Taiwan, 2002.</ref><ref>[https://web.archive.org/web/20070106074102/http://lcl2.di.uniroma1.it/~sclano/ Sclano, F.] and [http://www.dsi.uniroma1.it/~velardi/welcome.htm Velardi, P.]. [https://web.archive.org/web/20070214092612/http://lcl2.di.uniroma1.it/termextractor/ TermExtractor]: a Web Application to Learn the Shared Terminology of Emergent Web Communities. To appear in Proc. of the 3rd International Conference on Interoperability for Enterprise Software and Applications (I-ESA 2007). Funchal (Madeira Island), Portugal, March 28–30th, 2007.</ref><ref>P. Velardi, R. Navigli, P. D'Amadio. [https://ieeexplore.ieee.org/document/4629722/;jsessionid=45F3BD768F5CC0F3AE6AE218318A450F?arnumber=4629722 Mining the Web to Create Specialized Glossaries], IEEE Intelligent Systems, 23(5), IEEE Press, 2008, pp. 18-25.</ref><ref>Wermter J. and Hahn U. [http://portal.acm.org/citation.cfm?id=1088648 Finding New terminology in Very large Corpora], in Proc. of K-CAP'05, October 2–5, 2005, Banff, Alberta, Canada</ref><ref>Wong, W., Liu, W. & Bennamoun, M. (2007) [http://portal.acm.org/citation.cfm?id=1378245.1378253 Determining Termhood for Learning Domain Ontologies using Domain Prevalence and Tendency]. In: 6th Australasian Conference on Data Mining (AusDM); Gold Coast. {{ISBN|978-1-920682-51-4}}</ref><ref>Wong, W., Liu, W. & Bennamoun, M. (2007) [http://portal.acm.org/citation.cfm?id=1378254&jmp=cit&coll=GUIDE&dl=GUIDE Determining Termhood for Learning Domain Ontologies in a Probabilistic Framework]. In: 6th Australasian Conference on Data Mining (AusDM); Gold Coast. {{ISBN|978-1-920682-51-4}}</ref>


Typically, approaches to automatic term extraction make use of linguistic processors ([[part of speech tagging]], [[phrase chunking]]) to extract terminological candidates, i.e. syntactically plausible terminological [[noun phrase]]s, NPs (e.g. compounds "credit card", adjective-NPs "local tourist information office", and prepositional-NPs "board of directors" - in English, the first two constructs are the most frequent<ref>{{Cite book |doi = 10.1007/978-3-319-66939-7_19|chapter = SemCluster: Unsupervised Automatic Keyphrase Extraction Using Affinity Propagation|title = Advances in Computational Intelligence Systems|volume = 650|pages = 222–235|series = Advances in Intelligent Systems and Computing|year = 2018|last1 = Alrehamy|first1 = Hassan H|last2 = Walker|first2 = Coral|isbn = 978-3-319-66938-0}}</ref>). Terminological entries are then filtered from the candidate list using statistical and [[machine learning]] methods. Once filtered, because of their low ambiguity and high specificity, these terms are particularly useful for conceptualizing a knowledge domain or for supporting the creation of a [[Ontology (computer science)#Domain ontologies and upper ontologies|domain ontology]] or a terminology base. Furthermore, terminology extraction is a very useful starting point for [[semantic similarity]], [[knowledge management]], [[translation|human translation]] and [[machine translation]], etc.
Typically, approaches to automatic term extraction make use of linguistic processors ([[part of speech tagging]], [[phrase chunking]]) to extract terminological candidates, i.e. syntactically plausible terminological [[noun phrase]]s. Noun phrases include compounds (e.g. "credit card"), adjective noun phrases (e.g. "local tourist information office"), and prepositional noun phrases (e.g. "board of directors"). In English, the first two (compounds and adjective noun phrases) are the most frequent.<ref>{{Cite book |doi = 10.1007/978-3-319-66939-7_19|chapter = SemCluster: Unsupervised Automatic Keyphrase Extraction Using Affinity Propagation|title = Advances in Computational Intelligence Systems|volume = 650|pages = 222–235|series = Advances in Intelligent Systems and Computing|year = 2018|last1 = Alrehamy|first1 = Hassan H|last2 = Walker|first2 = Coral|isbn = 978-3-319-66938-0}}</ref> Terminological entries are then filtered from the candidate list using statistical and [[machine learning]] methods. Once filtered, because of their low ambiguity and high specificity, these terms are particularly useful for conceptualizing a knowledge domain or for supporting the creation of a [[Ontology (computer science)#Domain ontologies and upper ontologies|domain ontology]] or a terminology base. Furthermore, terminology extraction is a very useful starting point for [[semantic similarity]], [[knowledge management]], [[translation|human translation]] and [[machine translation]], etc.


==Bilingual terminology extraction==
==Bilingual terminology extraction==


The methods for terminology extraction can be applied to [[parallel corpus|parallel corpora]]. Combined with e.g. [[co-occurrence]] statistics, candidates for term translations can be obtained.<ref>Macken, Lieve, Els Lefever, and Veronique Hoste. "TExSIS: Bilingual terminology extraction from parallel corpora using chunk-based alignment." Terminology 19.1 (2013): 1-30.</ref> Bilingual terminology can be extracted also from comparable corpora<ref>
The methods for terminology extraction can be applied to [[parallel corpus|parallel corpora]]. Combined with e.g. [[co-occurrence]] statistics, candidates for term translations can be obtained.<ref>{{cite journal | last1 = Macken | first1 = Lieve | last2 = Lefever | first2 = Els | last3 = Hoste | first3 = Veronique | year = 2013 | title = TExSIS: Bilingual terminology extraction from parallel corpora using chunk-based alignment | url = https://biblio.ugent.be/publication/2128573/file/6771624 | journal = Terminology | volume = 19 | issue = 1| pages = 1–30 | doi = 10.1075/term.19.1.01mac | hdl = 1854/LU-2128573 | hdl-access = free }}</ref> Bilingual terminology can be extracted also from comparable corpora<ref>
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[[Category:Tasks of natural language processing]]
[[Category:Tasks of natural language processing]]
[[Category:Library science terminology|Extraction]]
[[Category:Library science terminology]]
[[Category:Computing terminology]]
[[Category:Computing terminology]]

Latest revision as of 20:54, 30 July 2024

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Terminology extraction (also known as term extraction, glossary extraction, term recognition, or terminology mining) is a subtask of information extraction. The goal of terminology extraction is to automatically extract relevant terms from a given corpus.[1]

In the semantic web era, a growing number of communities and networked enterprises started to access and interoperate through the internet. Modeling these communities and their information needs is important for several web applications, like topic-driven web crawlers,[2] web services,[3] recommender systems,[4] etc. The development of terminology extraction is also essential to the language industry.

One of the first steps to model a knowledge domain is to collect a vocabulary of domain-relevant terms, constituting the linguistic surface manifestation of domain concepts. Several methods to automatically extract technical terms from domain-specific document warehouses have been described in the literature.[5][6][7][8][9][10][11][12][13][14][15][16][17]

Typically, approaches to automatic term extraction make use of linguistic processors (part of speech tagging, phrase chunking) to extract terminological candidates, i.e. syntactically plausible terminological noun phrases. Noun phrases include compounds (e.g. "credit card"), adjective noun phrases (e.g. "local tourist information office"), and prepositional noun phrases (e.g. "board of directors"). In English, the first two (compounds and adjective noun phrases) are the most frequent.[18] Terminological entries are then filtered from the candidate list using statistical and machine learning methods. Once filtered, because of their low ambiguity and high specificity, these terms are particularly useful for conceptualizing a knowledge domain or for supporting the creation of a domain ontology or a terminology base. Furthermore, terminology extraction is a very useful starting point for semantic similarity, knowledge management, human translation and machine translation, etc.

Bilingual terminology extraction

[edit]

The methods for terminology extraction can be applied to parallel corpora. Combined with e.g. co-occurrence statistics, candidates for term translations can be obtained.[19] Bilingual terminology can be extracted also from comparable corpora[20] (corpora containing texts within the same text type, domain but not translations of documents between each other).

See also

[edit]

References

[edit]
  1. ^ Alrehamy, Hassan H; Walker, Coral (2018). "SemCluster: Unsupervised Automatic Keyphrase Extraction Using Affinity Propagation". Advances in Computational Intelligence Systems. Advances in Intelligent Systems and Computing. Vol. 650. pp. 222–235. doi:10.1007/978-3-319-66939-7_19. ISBN 978-3-319-66938-0.
  2. ^ Menczer F., Pant G. and Srinivasan P. Topic-Driven Crawlers: machine learning issues.
  3. ^ Fan J. and Kambhampati S. A Snapshot of Public Web Services, in ACM SIGMOD Record archive Volume 34 , Issue 1 (March 2005).
  4. ^ Yan Zheng Wei, Luc Moreau, Nicholas R. Jennings. A market-based approach to recommender systems, in ACM Transactions on Information Systems (TOIS), 23(3), 2005.
  5. ^ Bourigault D. and Jacquemin C. Term Extraction+Term Clustering: an integrated platform for computer-aided terminology Archived 2006-06-19 at the Wayback Machine, in Proc. of EACL, 1999.
  6. ^ Collier, N.; Nobata, C.; Tsujii, J. (2002). "Automatic acquisition and classification of terminology using a tagged corpus in the molecular biology domain". Terminology. 7 (2): 239–257. doi:10.1075/term.7.2.07col.
  7. ^ K. Frantzi, S. Ananiadou and H. Mima. (2000). Automatic recognition of multi-word terms: the C-value/NC-value method. In: C. Nikolau and C. Stephanidis (Eds.) International Journal on Digital Libraries, Vol. 3, No. 2., pp. 115-130.
  8. ^ K. Frantzi, S. Ananiadou and J. Tsujii. (1998) The C-value/NC-value Method of Automatic Recognition of Multi-word Terms, In: ECDL '98 Proceedings of the Second European Conference on Research and Advanced Technology for Digital Libraries, pp. 585-604. ISBN 3-540-65101-2
  9. ^ L. Kozakov; Y. Park; T. Fin; Y. Drissi; Y. Doganata & T. Cofino. (2004). "Glossary extraction and utilization in the information search and delivery system for IBM Technical Support" (PDF). IBM Systems Journal. 43 (3): 546–563. doi:10.1147/sj.433.0546.
  10. ^ Navigli R. and Velardi, P. Learning Domain Ontologies from Document Warehouses and Dedicated Web Sites. Computational Linguistics. 30 (2), MIT Press, 2004, pp. 151-179
  11. ^ Oliver, A. and Vàzquez, M. TBXTools: A Free, Fast and Flexible Tool for Automatic Terminology Extraction. Proceedings of Recent Advances in Natural Language Processing (RANLP 2015), 2015, pp. 473–479
  12. ^ Y. Park, R. J. Byrd, B. Boguraev. "Automatic glossary extraction: beyond terminology identification", International Conference On Computational Linguistics, Proceedings of the 19th international conference on Computational linguistics - Taipei, Taiwan, 2002.
  13. ^ Sclano, F. and Velardi, P.. TermExtractor: a Web Application to Learn the Shared Terminology of Emergent Web Communities. To appear in Proc. of the 3rd International Conference on Interoperability for Enterprise Software and Applications (I-ESA 2007). Funchal (Madeira Island), Portugal, March 28–30th, 2007.
  14. ^ P. Velardi, R. Navigli, P. D'Amadio. Mining the Web to Create Specialized Glossaries, IEEE Intelligent Systems, 23(5), IEEE Press, 2008, pp. 18-25.
  15. ^ Wermter J. and Hahn U. Finding New terminology in Very large Corpora, in Proc. of K-CAP'05, October 2–5, 2005, Banff, Alberta, Canada
  16. ^ Wong, W., Liu, W. & Bennamoun, M. (2007) Determining Termhood for Learning Domain Ontologies using Domain Prevalence and Tendency. In: 6th Australasian Conference on Data Mining (AusDM); Gold Coast. ISBN 978-1-920682-51-4
  17. ^ Wong, W., Liu, W. & Bennamoun, M. (2007) Determining Termhood for Learning Domain Ontologies in a Probabilistic Framework. In: 6th Australasian Conference on Data Mining (AusDM); Gold Coast. ISBN 978-1-920682-51-4
  18. ^ Alrehamy, Hassan H; Walker, Coral (2018). "SemCluster: Unsupervised Automatic Keyphrase Extraction Using Affinity Propagation". Advances in Computational Intelligence Systems. Advances in Intelligent Systems and Computing. Vol. 650. pp. 222–235. doi:10.1007/978-3-319-66939-7_19. ISBN 978-3-319-66938-0.
  19. ^ Macken, Lieve; Lefever, Els; Hoste, Veronique (2013). "TExSIS: Bilingual terminology extraction from parallel corpora using chunk-based alignment". Terminology. 19 (1): 1–30. doi:10.1075/term.19.1.01mac. hdl:1854/LU-2128573.
  20. ^ Sharoff, Serge; Rapp, Reinhard; Zweigenbaum, Pierre; Fung, Pascale (2013), Building and Using Comparable Corpora (PDF), Berlin: Springer-Verlag
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