@inproceedings{5d811a2f2c9c4989a64becc6f527808e,
title = "Learning to exploit structured resources for lexical inference",
abstract = "Massive knowledge resources, such as Wikidata, can provide valuable information for lexical inference, especially for proper-names. Prior resource-based approaches typically select the subset of each resource{\textquoteright}s relations which are relevant for a particular given task. The selection process is done manually, limiting these approaches to smaller resources such as WordNet, which lacks coverage of proper-names and recent terminology. This paper presents a supervised framework for automatically selecting an optimized subset of resource relations for a given target inference task. Our approach enables the use of large-scale knowledge resources, thus providing a rich source of high-precision inferences over proper-names.1",
author = "Vered Shwartz and Omer Levy and Ido Dagan and Jacob Goldberger",
note = "Publisher Copyright: {\textcopyright} 2015 Association for Computational Linguistics.; 19th Conference on Computational Natural Language Learning, CoNLL 2015 ; Conference date: 30-07-2015 Through 31-07-2015",
year = "2015",
doi = "10.18653/v1/k15-1018",
language = "الإنجليزيّة",
series = "CoNLL 2015 - 19th Conference on Computational Natural Language Learning, Proceedings",
publisher = "Association for Computational Linguistics (ACL)",
pages = "175--184",
booktitle = "CoNLL 2015 - 19th Conference on Computational Natural Language Learning, Proceedings",
address = "الولايات المتّحدة",
}