Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/41954
Title: Combining relational and attributional similarity for semantic relation classification
Authors: Nakov, P. 
Kozareva, Z.
Issue Date: 2011
Source: Nakov, 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.
Abstract: We 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.
Source Title: International Conference Recent Advances in Natural Language Processing, RANLP
URI: http://scholarbank.nus.edu.sg/handle/10635/41954
ISSN: 13138502
Appears in Collections:Staff Publications

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