Distributional memory: a general framework for corpus-based semantics

Mostra el registre complet Registre parcial de l'ítem

• dc.contributor.author Baroni, Marco
• dc.contributor.author Lenci, Alessandro
• dc.date.accessioned 2020-12-15T08:54:37Z
• dc.date.available 2020-12-15T08:54:37Z
• dc.date.issued 2010
• dc.description.abstract Research into corpus-based semantics has focused on the development of ad hoc models that treat single tasks, or sets of closely related tasks, as unrelated challenges to be tackled by extracting different kinds of distributional information from the corpus. As an alternative to this “one task, one model” approach, the Distributional Memory framework extracts distributional information once and for all from the corpus, in the form of a set of weighted word-link-word tuples arranged into a third-order tensor. Different matrices are then generated from the tensor, and their rows and columns constitute natural spaces to deal with different semantic problems. In this way, the same distributional information can be shared across tasks such as modeling word similarity judgments, discovering synonyms, concept categorization, predicting selectional preferences of verbs, solving analogy problems, classifying relations between word pairs, harvesting qualia structures with patterns or example pairs, predicting the typical properties of concepts, and classifying verbs into alternation classes. Extensive empirical testing in all these domains shows that a Distributional Memory implementation performs competitively against task-specific algorithms recently reported in the literature for the same tasks, and against our implementations of several state-of-the-art methods. The Distributional Memory approach is thus shown to be tenable despite the constraints imposed by its multi-purpose nature.en
• dc.format.mimetype application/pdf
• dc.identifier.citation Baroni M, Lenci A. Distributional memory: a general framework for corpus-based semantics. Computational linguistics. 2010 Dec 15;36(4):673-721. DOI: 10.1162/coli_a_00016
• dc.identifier.doi http://dx.doi.org/10.1162/coli_a_00016
• dc.identifier.issn 0891-2017
• dc.identifier.uri http://hdl.handle.net/10230/46048
• dc.language.iso eng
• dc.publisher MIT Press
• dc.relation.ispartof Computational linguistics. 2010 Dec 15;36(4):673-721
• dc.rights Computational Linguistics Computational Linguistics is Open Access. All content is freely available in electronic format (Full text HTML, PDF, and PDF Plus) to readers across the globe. All articles are published under a CC BY-NC-ND 4.0 license. (https://creativecommons.org/licenses/by-nc-nd/4.0/)
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
• dc.title Distributional memory: a general framework for corpus-based semanticsen
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion