Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/41954
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dc.titleCombining relational and attributional similarity for semantic relation classification
dc.contributor.authorNakov, P.
dc.contributor.authorKozareva, Z.
dc.date.accessioned2013-07-04T08:39:49Z
dc.date.available2013-07-04T08:39:49Z
dc.date.issued2011
dc.identifier.citationNakov, P.,Kozareva, Z. (2011). Combining relational and attributional similarity for semantic relation classification. International Conference Recent Advances in Natural Language Processing, RANLP : 323-330. ScholarBank@NUS Repository.
dc.identifier.issn13138502
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/41954
dc.description.abstractWe combine relational and attributional similarity for the task of identifying instances of semantic relations, such as PRODUCT-PRODUCER and ORIGINENTITY, between nominals in text. We use no pre-existing lexical resources, thus simulating a realistic real-world situation, where the coverage of any such resource is limited. Instead, we mine the Web to automatically extract patterns (verbs, prepositions and coordinating conjunctions) expressing the relationship between the relation arguments, as well as hypernyms and co-hyponyms of the arguments, which we use in instance-based classifiers. The evaluation on the dataset of SemEval-1 Task 4 shows an improvement over the state-of-the-art for the case where using manually annotated WordNet senses is not allowed.
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentCOMPUTER SCIENCE
dc.description.sourcetitleInternational Conference Recent Advances in Natural Language Processing, RANLP
dc.description.page323-330
dc.identifier.isiutNOT_IN_WOS
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