We describe here a methodology to combine two different techniques for Semantic Relation Extraction from texts. On the one hand, generic lexicosyntactic patterns are applied to the linguistically analyzed corpus to detect a first set of pairs of co-occurring words, possibly involved in "syntagmatic" relations. On the other hand, a statistical unsupervised association system is used to obtain a second set of pairs of "distributionally similar" terms, that appear to occur in similar contexts, thus possibly involved in "paradigmatic" relations. The approach aims at learning ontological information by filtering the candidate relations obtained through generic lexico-syntactic patterns and by labelling the anonymous relations obtained through the statistical system. The resulting set of relations can be used to enrich existing ontologies and for semantic annotation of documents or web pages.
Combining statistical techniques and lexico-syntactic patterns for semantic relations extraction from text
Giovannetti E;Marchi S;Montemagni S
2008
Abstract
We describe here a methodology to combine two different techniques for Semantic Relation Extraction from texts. On the one hand, generic lexicosyntactic patterns are applied to the linguistically analyzed corpus to detect a first set of pairs of co-occurring words, possibly involved in "syntagmatic" relations. On the other hand, a statistical unsupervised association system is used to obtain a second set of pairs of "distributionally similar" terms, that appear to occur in similar contexts, thus possibly involved in "paradigmatic" relations. The approach aims at learning ontological information by filtering the candidate relations obtained through generic lexico-syntactic patterns and by labelling the anonymous relations obtained through the statistical system. The resulting set of relations can be used to enrich existing ontologies and for semantic annotation of documents or web pages.| Campo DC | Valore | Lingua |
|---|---|---|
| dc.authority.orgunit | Istituto di linguistica computazionale "Antonio Zampolli" - ILC | - |
| dc.authority.people | Giovannetti E | it |
| dc.authority.people | Marchi S | it |
| dc.authority.people | Montemagni S | it |
| dc.collection.id.s | 71c7200a-7c5f-4e83-8d57-d3d2ba88f40d | * |
| dc.collection.name | 04.01 Contributo in Atti di convegno | * |
| dc.contributor.appartenenza | Istituto di linguistica computazionale "Antonio Zampolli" - ILC | * |
| dc.contributor.appartenenza.mi | 918 | * |
| dc.date.accessioned | 2024/02/19 19:40:09 | - |
| dc.date.available | 2024/02/19 19:40:09 | - |
| dc.date.issued | 2008 | - |
| dc.description.abstracteng | We describe here a methodology to combine two different techniques for Semantic Relation Extraction from texts. On the one hand, generic lexicosyntactic patterns are applied to the linguistically analyzed corpus to detect a first set of pairs of co-occurring words, possibly involved in "syntagmatic" relations. On the other hand, a statistical unsupervised association system is used to obtain a second set of pairs of "distributionally similar" terms, that appear to occur in similar contexts, thus possibly involved in "paradigmatic" relations. The approach aims at learning ontological information by filtering the candidate relations obtained through generic lexico-syntactic patterns and by labelling the anonymous relations obtained through the statistical system. The resulting set of relations can be used to enrich existing ontologies and for semantic annotation of documents or web pages. | - |
| dc.description.affiliations | ILC-CNR | - |
| dc.description.allpeople | Giovannetti, E; Marchi, S; Montemagni, S | - |
| dc.description.allpeopleoriginal | Giovannetti E.; Marchi S.; Montemagni S. | - |
| dc.description.fulltext | none | en |
| dc.description.numberofauthors | 3 | - |
| dc.identifier.scopus | 2-s2.0-84874234733 | - |
| dc.identifier.uri | https://hdl.handle.net/20.500.14243/65082 | - |
| dc.identifier.url | http://sunsite.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-426/swap2008_submission_54.pdf | - |
| dc.relation.alleditors | Aldo Gangemi, Johannes Keizer, Valentina Presutti, Heiko Stoermer | - |
| dc.relation.conferencedate | 15-17 December 2008 | - |
| dc.relation.conferencename | SWAP 2008 - Semantic Web Applications and Perspectives | - |
| dc.relation.conferenceplace | Roma | - |
| dc.subject.keywords | Ontology Learning from Text | - |
| dc.subject.keywords | Semantic Relation Extraction | - |
| dc.subject.keywords | Lexico-syntactic Patterns | - |
| dc.subject.keywords | Distributional Similarity | - |
| dc.subject.singlekeyword | Ontology Learning from Text | * |
| dc.subject.singlekeyword | Semantic Relation Extraction | * |
| dc.subject.singlekeyword | Lexico-syntactic Patterns | * |
| dc.subject.singlekeyword | Distributional Similarity | * |
| dc.title | Combining statistical techniques and lexico-syntactic patterns for semantic relations extraction from text | en |
| dc.type.driver | info:eu-repo/semantics/conferenceObject | - |
| dc.type.full | 04 Contributo in convegno::04.01 Contributo in Atti di convegno | it |
| dc.type.miur | 273 | - |
| dc.type.referee | Sì, ma tipo non specificato | - |
| dc.ugov.descaux1 | 84706 | - |
| iris.orcid.lastModifiedDate | 2024/06/12 10:17:31 | * |
| iris.orcid.lastModifiedMillisecond | 1718180251464 | * |
| iris.scopus.extIssued | 2008 | - |
| iris.scopus.extTitle | Combining statistical techniques and lexico-syntactic patterns for semantic relations extraction from text | - |
| iris.scopus.ideLinkStatusDate | 2024/06/12 10:17:31 | * |
| iris.scopus.ideLinkStatusMillisecond | 1718180251504 | * |
| iris.sitodocente.maxattempts | 4 | - |
| scopus.authority.anceserie | CEUR WORKSHOP PROCEEDINGS###1613-0073 | * |
| scopus.category | 1700 | * |
| scopus.contributor.affiliation | Consiglio Nazionale delle Ricerche | - |
| scopus.contributor.affiliation | Consiglio Nazionale delle Ricerche | - |
| scopus.contributor.affiliation | Consiglio Nazionale delle Ricerche | - |
| scopus.contributor.afid | 60008941 | - |
| scopus.contributor.afid | 60008941 | - |
| scopus.contributor.afid | 60008941 | - |
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| scopus.contributor.auid | 27567818000 | - |
| scopus.contributor.auid | 15056781100 | - |
| scopus.contributor.country | Italy | - |
| scopus.contributor.country | Italy | - |
| scopus.contributor.country | Italy | - |
| scopus.contributor.dptid | - | |
| scopus.contributor.dptid | - | |
| scopus.contributor.dptid | - | |
| scopus.contributor.name | Emiliano | - |
| scopus.contributor.name | Simone | - |
| scopus.contributor.name | Simonetta | - |
| scopus.contributor.subaffiliation | Istituto di Linguistica Computazionale; | - |
| scopus.contributor.subaffiliation | Istituto di Linguistica Computazionale; | - |
| scopus.contributor.subaffiliation | Istituto di Linguistica Computazionale; | - |
| scopus.contributor.surname | Giovannetti | - |
| scopus.contributor.surname | Marchi | - |
| scopus.contributor.surname | Montemagni | - |
| scopus.date.issued | 2008 | * |
| scopus.description.abstracteng | We describe here a methodology to combine two different techniques for Semantic Relation Extraction from texts. On the one hand, generic lexicosyntactic patterns are applied to the linguistically analyzed corpus to detect a first set of pairs of co-occurring words, possibly involved in "syntagmatic" relations. On the other hand, a statistical unsupervised association system is used to obtain a second set of pairs of "distributionally similar" terms, that appear to occur in similar contexts, thus possibly involved in "paradigmatic" relations. The approach aims at learning ontological information by filtering the candidate relations obtained through generic lexico-syntactic patterns and by labelling the anonymous relations obtained through the statistical system. The resulting set of relations can be used to enrich existing ontologies and for semantic annotation of documents or web pages. | * |
| scopus.description.allpeopleoriginal | Giovannetti E.; Marchi S.; Montemagni S. | * |
| scopus.differences | scopus.relation.conferencename | * |
| scopus.differences | scopus.authority.anceserie | * |
| scopus.differences | scopus.subject.keywords | * |
| scopus.differences | scopus.relation.conferencedate | * |
| scopus.differences | scopus.relation.conferenceplace | * |
| scopus.differences | scopus.language.iso | * |
| scopus.differences | scopus.relation.volume | * |
| scopus.document.type | cp | * |
| scopus.document.types | cp | * |
| scopus.identifier.pui | 368392587 | * |
| scopus.identifier.scopus | 2-s2.0-84874234733 | * |
| scopus.journal.sourceid | 21100218356 | * |
| scopus.language.iso | eng | * |
| scopus.relation.conferencedate | 2008 | * |
| scopus.relation.conferencename | 5th Workshop on Semantic Web Applications and Perspectives, SWAP 2008 | * |
| scopus.relation.conferenceplace | Rome, ita | * |
| scopus.relation.volume | 426 | * |
| scopus.subject.keywords | Distributional similarity; Lexico-syntactic patterns; Ontology learning from text; Semantic relation extraction; | * |
| scopus.title | Combining statistical techniques and lexico-syntactic patterns for semantic relations extraction from text | * |
| scopus.titleeng | Combining statistical techniques and lexico-syntactic patterns for semantic relations extraction from text | * |
| Appare nelle tipologie: | 04.01 Contributo in Atti di convegno | |
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