Tailoring the automated construction of large-scale taxonomies using the web

It has long been a dream to have available a single, centralized, semantic thesaurus or terminology taxonomy to support research in a variety of fields. Much human and computational effort has gone into constructing such resources, including the original WordNet and subsequent wordnets in various la...

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Published in	Language Resources and Evaluation Vol. 47; no. 3; pp. 859 - 890
Main Authors	Kozareva, Zornitsa, Hovy, Eduard
Format	Journal Article
Language	English
Published	Dordrecht Springer 01.09.2013 Springer Netherlands Springer Nature B.V
Subjects	Algorithms Animal genetics Automated Automatic text analysis Automation Biological taxonomies Computational Linguistics Computer Science Construction Dictionaries Domain ontologies English language Hierarchical relationships Hierarchies Humans International conferences Jargon Language Language and Literature Languages Linguistics Mammals Natural language processing Ontologies Ontology Original Paper Recognition Semantic analysis Semantics Social Sciences Taxonomy Terminology Texts Thesauri United States > US Text mining Ontology induction Hyponym and hypernym learning Wordnet evaluation
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Abstract	It has long been a dream to have available a single, centralized, semantic thesaurus or terminology taxonomy to support research in a variety of fields. Much human and computational effort has gone into constructing such resources, including the original WordNet and subsequent wordnets in various languages. To produce such resources one has to overcome well-known problems in achieving both wide coverage and internal consistency within a single wordnet and across many wordnets. In particular, one has to ensure that alternative valid taxonomizations covering the same basic terms are recognized and treated appropriately. In this paper we describe a pipeline of new, powerful, minimally supervised, automated algorithms that can be used to construct terminology taxonomies and wordnets, in various languages, by harvesting large amounts of online domain-specific or general text. We illustrate the effectiveness of the algorithms both to build localized, domain-specific wordnets and to highlight and investigate certain deeper ontological problems such as parallel generalization hierarchies. We show shortcomings and gaps in the manually-constructed English WordNet in various domains.
AbstractList	It has long been a dream to have available a single, centralized, semantic thesaurus or terminology taxonomy to support research in a variety of fields. Much human and computational effort has gone into constructing such resources, including the original WordNet and subsequent wordnets in various languages. To produce such resources one has to overcome well-known problems in achieving both wide coverage and internal consistency within a single wordnet and across many wordnets. In particular, one has to ensure that alternative valid taxonomizations covering the same basic terms are recognized and treated appropriately. In this paper we describe a pipeline of new, powerful, minimally supervised, automated algorithms that can be used to construct terminology taxonomies and wordnets, in various languages, by harvesting large amounts of online domain-specific or general text. We illustrate the effectiveness of the algorithms both to build localized, domain-specific wordnets and to highlight and investigate certain deeper ontological problems such as parallel generalization hierarchies. We show shortcomings and gaps in the manually-constructed English WordNet in various domains. It has long been a dream to have available a single, centralized, semantic thesaurus or terminology taxonomy to support research in a variety of fields. Much human and computational effort has gone into constructing such resources, including the original WordNet and subsequent wordnets in various languages. To produce such resources one has to overcome well-known problems in achieving both wide coverage and internal consistency within a single wordnet and across many wordnets. In particular, one has to ensure that alternative valid taxonomizations covering the same basic terms are recognized and treated appropriately. In this paper we describe a pipeline of new, powerful, minimally supervised, automated algorithms that can be used to construct terminology taxonomies and wordnets, in various languages, by harvesting large amounts of online domain-specific or general text. We illustrate the effectiveness of the algorithms both to build localized, domain-specific wordnets and to highlight and investigate certain deeper ontological problems such as parallel generalization hierarchies. We show shortcomings and gaps in the manually-constructed English WordNet in various domains. Adapted from the source document Issue Title: Special Issues: "Computational Semantic Analysis of Language: SemEval-2010" and "Wordnets and Relations" It has long been a dream to have available a single, centralized, semantic thesaurus or terminology taxonomy to support research in a variety of fields. Much human and computational effort has gone into constructing such resources, including the original WordNet and subsequent wordnets in various languages. To produce such resources one has to overcome well-known problems in achieving both wide coverage and internal consistency within a single wordnet and across many wordnets. In particular, one has to ensure that alternative valid taxonomizations covering the same basic terms are recognized and treated appropriately. In this paper we describe a pipeline of new, powerful, minimally supervised, automated algorithms that can be used to construct terminology taxonomies and wordnets, in various languages, by harvesting large amounts of online domain-specific or general text. We illustrate the effectiveness of the algorithms both to build localized, domain-specific wordnets and to highlight and investigate certain deeper ontological problems such as parallel generalization hierarchies. We show shortcomings and gaps in the manually-constructed English WordNet in various domains.[PUBLICATION ABSTRACT]
Author	Hovy, Eduard Kozareva, Zornitsa
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References	FleissJ.Measuring nominal scale agreement among many ratersPsychological Bulletin197176537838210.1037/h0031619 Ide, N., & Veronis, J. (1994). Machine readable dictionaries: What have we learned, where do we go. In Proceedings of the post-COLING 94 intl. workshop on directions of lexical research, Beijing, pp. 137–146. Katz, B., & Lin, J. (2003). Selectively using relations to improve precision in question answering. In Proceedings of the EACL-2003 workshop on natural language processing for question answering, pp. 43–50. NavigliR.PonzettoP.BabelNet: The automatic construction, evaluation and application of a wide-coverage multilingual semantic networkJournal of Artificial Intelligence2012193217250 Wilks, Y., Fass, D., ming Guo, C., Mcdonald, J. E., Plate, T., & Slator, B. M. (1988). Machine tractable dictionaries as tools and resources for natural language processing. In Proceedings of the 12th conference on computational linguistics, Association for Computational Linguistics, Morristown, NJ, USA, pp. 750–755. MillerG. A.WordNet: a lexical database for englishCommunications of the ACM1995383941 Bateman, J. A., Kasper, R. T., Moore, J. D., & Whitney, R. A. (1989). A general organization of knowledge for natural language processing: The penman upper model. Unpublished research report, USC/Information Sciences Institute, Marina del Rey. Robkop, K., Thoongsup, S., Charoenporn, T., Sornlertlamvanich, V., & Isahara, H. (2010). WNMS: Connecting the distributed WordNet in the case of Asian WordNet the 5th international conference of the global WordNet association (GWC-2010), Mumbai, India. VelardiP.RobertoN.PierluigiD.Mining the web to create specialized glossariesJournal of IEEE Intelligent Systems20082351825 Snow, R., Jurafsky, D., & Ng, A. Y. (2006). Semantic taxonomy induction from heterogenous evidence. In Proceedings of the international conference on computational linguistics (COLING) and the annual meeting of the association for computational linguistics (ACL). Cuadros, M., & Rigau, G. (2008). KnowNet: Building a large net of knowledge from the web. The 22nd international conference on computational linguistics (Coling’08), UK, Manchester. Pasca, M. (2004). Acquisition of categorized named entities for web search. In Proceedings of the thirteenth ACM international conference on information and knowledge management (CIKM), pp 137–145. Richardson, S. D., Dolan, W. B., & Vanderwende, L. (1998). Mindnet: Acquiring and structuring semantic information from text. In Proceedings of the 36th annual meeting of the association for computational linguistics and 17th international conference on computational linguistics—Volume 2 (ACL ’98), (Vol. 2). Association for Computational Linguistics, Stroudsburg, PA, USA, pp. 1098–1102. Navigli, R., Velardi, P., & Faralli, S. (2011). A graph-based algorithm for inducing lexical taxonomies from scratch. In Proceedings of the Twenty-Second international joint conference on artificial intelligence—volume volume three. IJCAI’11, pp. 1872–1877. Snow, R., Jurafsky, D., & Ng, A.Y. (2005). Learning syntactic patterns for automatic hypernym discovery. In L. K. Saul, Y. Weiss, & L. Bottou (Eds.), Advances in neural information processing systems (Vol. 17, pp. 1297–1304). EtzioniO.CafarellaM.DowneyD.PopescuA. M.ShakedT.SoderlandS.WeldD.S.YatesA.Unsupervised named-entity extraction from the web: An experimental studyArtificial Intelligence200516519113410.1016/j.artint.2005.03.001 Rigau, G., Rodriguez, H., & Agirre, E. (1998). Building accurate semantic taxonomies from monolingual MRDs. In Proceedings of the 36th annual meeting of the association for computational linguistics and 17th international conference on computational linguistics—Volume 2 (ACL ’98), (Vol. 2). Association for Computational Linguistics, Stroudsburg, PA, USA, pp. 1103–1109. Roberto, N., Velardi, P., & Faralli, S. (2011). A graph-based algorithm for inducing lexical taxonomies from scratch. In Proceedings of IJCAI 2011, pp. 1872–1877. Vossen, P., Hofmann, K., Rijke, M., Tjong, E., Sang, K., & Deschacht, K. (2008). The Cornetto database: Architecture and user-scenarios. In Proceedings of the fourth international GlobalWordNet conference—GWC. Amsler, R. A. (1981). A taxonomy for english nouns and verbs. In: Proceedings of the 19th annual meeting on association for computational linguistics, Association for Computational Linguistics, Morristown, NJ, USA, pp. 133–138. Moldovan, D. I., Harabagiu, S. M., Pasca, M., Mihalcea, R., Goodrum, R., Girju, R. et al. (1999). Lasso: A tool for surfing the answer net. In Proceedings of the TREC conference. GeorgeA. M.WordNet: A lexical database for englishProceedings of Communications of the ACM1995383941 Atserias, J., Villarejo, L., Rigau, G., Agirre, E., Carroll, J., Magnini, B., et al. (2004). The MEANING multilingual central repository. In Proceedings of the second international WordNet conference. pp. 80–210. Szpektor, I., Dagan, I., Bar-Haim, R., & Goldberger, J. (2008). Contextual preferences. In Proceedings of the annual meeting of the association for computational linguistics (ACL), pp. 683–691. Moravcsik, J. M. E. (1981). How do words get their meanings? The Journal of Philosophy, 78 1. Kozareva, Z., Riloff, E., & Hovy, E. H. (2008). Semantic class learning from the web with hyponym pattern linkage graphs. In Proceedings of the NAACL-HLT conference, pp. 1048–1056. Navigli, R., Velardi, P., Cucchiarelli, A., Neri, F., & Cucchiarelli, R. (2004). Extending and enriching WordNet with OntoLearn. In Proceedings of the second global wordnet conference 2004 (GWC 2004). pp. 279–284. PustejovskyJ.The generative lexicon1995Cambridge, MAMIT Press Peters, I. (2009). Folksonomies. Indexing and retrieval in web 2.0. Berlin: De Gruyter Saur. Pennacchiotti, M., & Pantel P. (2006). Ontologizing semantic relations. In Proceedings of the international conference on computational linguistics (COLING) and the annual meeting of the association for computational linguistics (ACL), pp. 793–800. Glickman, O., Dagan, I., & Koppel, M. (2005). A probabilistic classification approach for lexical textual entailment. In Proceedings of the twentieth national conference on artificial intelligence and the seventeenth innovative applications of artificial intelligence conference, pp. 1050–1055. Banko, M. (2009). Open information extraction from the web. Ph.D. Dissertation from University of Washington. Hovy, E. H. (2002). Comparing sets of semantic relations in ontologies. In R. Green, C. A. Bean, & S. H. Myaeng (Eds.), The semantics of relationships: An interdisciplinary perspective, pp. 91–110. Lin, D. (1998). Automatic retrieval and clustering of similar words. In Proceedings of the 17th international conference on computational linguistics (COLING), pp. 768–774. FellbaumC.WordNet: An on-line lexical database and some of its applications1998Cambridge, MA.MIT Press Widdows, D. (2003). Unsupervised methods for developing taxonomies by combining syntactic and statistical information. In Proceedings of the HLT-NAACL conference. Agirre, E., & Lopez de Lacalle, O. (2004). Publicly available topic signatures for all WordNet nominal senses. In Proceedings of the 4rd international conference on languages resources and evaluations (LREC). Lisbon, Portugal. Ponzetto, S., & Navigli, R. (2010). Knowledge-rich word sense disambiguation rivaling supervised systems. In Proceedings of the 48th annual meeting of the association for computational linguistics (ACL 2010), Uppsala, Sweden. Riloff, E., & Jones, R. (1999). Learning dictionaries for information extraction by multi-level bootstrapping. In Proceedings of the sixteenth national conference on artificial intelligence (AAAI), pp. 474–479. Schmid, H. (1994). Probabilistic part-of-speech tagging using decision trees. In Proceedings of the international conference on new methods in language processing, pp. 4449. Ritter, A., Soderland, S., & Etzioni, O., (2009). What is this, anyway: Automatic hypernym discovery. In Proceedings of the AAAI spring symposium on learning by reading and learning to read. Hovy, E. H., Kozareva, Z., & Riloff, E. (2009). Toward completeness in concept extraction and classification. In Proceedings of the 2009 conference on empirical methods in natural language processing (EMNLP), pp. 948–957. Suchanek, F. M., Kasneci, G., & Weikum, G. (2007). Yago: A core of semantic knowledge. In Proceedings of the 16th international conference on World Wide Web (WWW), pp. 697–706. Pantel, P., Crestan, E., Borkovsky, A., Popescu, A. M., & Vyas, V. (2009). Web-scale distributional similarity and entity set expansion. In Proceedings of the conference on empirical methods in natural language processing (EMNLP), pp. 938–947. Vossen, P. (Ed.). (1998). EuroWordNet: A multilingual database with lexical semantic networks. Dordrecht, The Netherlands: Kluwer. Rosch, E. (1978). Principles of categorization. In Cognition and Categorization, pp. 27–48 Lenat, D. B., & Guha, R. V. (1990). Building large knowledge-based systems. reading. Boston: Addison-Wesley. Hearst, M. (1992). Automatic acquisition of hyponyms from large text corpora. In Proceedings of the 14th conference on computational linguistics, pp. 539–545. Lin, D., & Pantel, P. (2002). Concept discovery from text. In Proceedings of the 19th international conference on computational linguistics (COLING), pp. 1–7. Pantel, P., & Pennacchiotti, M. (2006). Espresso: Leveraging generic patterns for automatically harvesting semantic relations. In Proceedings of 21st international conference on computational linguistics (COLING) and 44th annual meeting of the association for computational linguistics (ACL). Yang, H., & Callan, J. (2009). A metric-based framework for automatic taxonomy induction. In 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 (ACL-IJCNLP) (Vol. 1, pp. 271–279. Ritter, A., & Mausam, O.E. (2010). A latent dirichlet allocation method for selectional pref 9229_CR32 9229_CR31 9229_CR34 9229_CR33 (9229_CR12) 1998 9229_CR30 A. M. George (9229_CR28) 1995; 38 9229_CR39 9229_CR36 9229_CR35 9229_CR38 9229_CR37 9229_CR43 9229_CR42 9229_CR45 9229_CR44 9229_CR41 9229_CR40 9229_CR6 O. Etzioni (9229_CR11) 2005; 165 9229_CR7 9229_CR8 9229_CR9 9229_CR2 9229_CR3 9229_CR4 9229_CR5 9229_CR47 9229_CR49 9229_CR1 9229_CR48 9229_CR10 9229_CR54 9229_CR53 9229_CR56 9229_CR55 9229_CR50 9229_CR52 9229_CR51 9229_CR19 9229_CR14 9229_CR58 9229_CR57 9229_CR16 9229_CR15 9229_CR59 9229_CR21 9229_CR20 9229_CR23 9229_CR22 9229_CR61 9229_CR60 9229_CR29 J. Pustejovsky (9229_CR46) 1995 9229_CR25 9229_CR24 9229_CR27 J. Fleiss (9229_CR13) 1971; 76 9229_CR26
References_xml	– ident: 9229_CR26 doi: 10.3115/980432.980696 – ident: 9229_CR3 doi: 10.3115/980691.980749 – ident: 9229_CR1 doi: 10.3115/981923.981959 – ident: 9229_CR27 doi: 10.3115/1072228.1072372 – ident: 9229_CR42 – ident: 9229_CR23 – ident: 9229_CR8 – ident: 9229_CR56 – volume: 38 start-page: 39 year: 1995 ident: 9229_CR28 publication-title: Proceedings of Communications of the ACM contributor: fullname: A. M. George – ident: 9229_CR39 doi: 10.3115/1220175.1220190 – ident: 9229_CR52 – ident: 9229_CR37 – ident: 9229_CR45 – ident: 9229_CR59 doi: 10.3115/1073445.1073481 – ident: 9229_CR55 doi: 10.1145/1242572.1242667 – ident: 9229_CR44 doi: 10.1515/9783598441851 – ident: 9229_CR49 – ident: 9229_CR54 doi: 10.3115/1220175.1220276 – ident: 9229_CR24 – ident: 9229_CR61 doi: 10.1145/219717.219748 – ident: 9229_CR22 doi: 10.3115/1075527.1075588 – ident: 9229_CR51 – ident: 9229_CR38 – ident: 9229_CR30 – ident: 9229_CR41 doi: 10.1145/1031171.1031194 – ident: 9229_CR34 – ident: 9229_CR14 doi: 10.3115/1073445.1073456 – ident: 9229_CR19 – volume-title: WordNet: An on-line lexical database and some of its applications year: 1998 ident: 9229_CR12 – ident: 9229_CR48 – ident: 9229_CR31 doi: 10.2307/2025394 – ident: 9229_CR2 – ident: 9229_CR25 – volume: 76 start-page: 378 issue: 5 year: 1971 ident: 9229_CR13 publication-title: Psychological Bulletin doi: 10.1037/h0031619 contributor: fullname: J. Fleiss – ident: 9229_CR16 doi: 10.3115/992133.992154 – volume-title: The generative lexicon year: 1995 ident: 9229_CR46 contributor: fullname: J. Pustejovsky – ident: 9229_CR58 – ident: 9229_CR20 doi: 10.1007/978-94-017-0073-3_6 – ident: 9229_CR6 – ident: 9229_CR5 doi: 10.3115/991719.991789 – ident: 9229_CR43 doi: 10.3115/1220175.1220275 – ident: 9229_CR35 – ident: 9229_CR50 – volume: 165 start-page: 91 issue: 1 year: 2005 ident: 9229_CR11 publication-title: Artificial Intelligence doi: 10.1016/j.artint.2005.03.001 contributor: fullname: O. Etzioni – ident: 9229_CR40 doi: 10.3115/1699571.1699635 – ident: 9229_CR47 – ident: 9229_CR9 doi: 10.3115/1599081.1599102 – ident: 9229_CR29 doi: 10.1007/978-3-642-04930-9_66 – ident: 9229_CR33 doi: 10.1016/j.artint.2012.07.001 – ident: 9229_CR4 doi: 10.3115/980691.980750 – ident: 9229_CR57 doi: 10.1007/978-94-017-1491-4 – ident: 9229_CR10 doi: 10.3115/1220175.1220213 – ident: 9229_CR21 doi: 10.3115/1699571.1699636 – ident: 9229_CR7 – ident: 9229_CR53 – ident: 9229_CR36 – ident: 9229_CR60 doi: 10.3115/1687878.1687918 – ident: 9229_CR15 – ident: 9229_CR32
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Snippet	It has long been a dream to have available a single, centralized, semantic thesaurus or terminology taxonomy to support research in a variety of fields. Much... Issue Title: Special Issues: "Computational Semantic Analysis of Language: SemEval-2010" and "Wordnets and Relations" It has long been a dream to have...
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SubjectTerms	Algorithms Animal genetics Automated Automatic text analysis Automation Biological taxonomies Computational Linguistics Computer Science Construction Dictionaries Domain ontologies English language Hierarchical relationships Hierarchies Humans International conferences Jargon Language Language and Literature Languages Linguistics Mammals Natural language processing Ontologies Ontology Original Paper Recognition Semantic analysis Semantics Social Sciences Taxonomy Terminology Texts Thesauri
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