Difference between revisions of "Expanding Textual Entailment Corpora Fromwikipedia Using Co-Training"
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| − | '''Expanding Textual Entailment Corpora Fromwikipedia Using Co-Training''' - scientific work related to Wikipedia quality published in 2010, written by Fabio Massimo Zanzotto and Marco Pennacchiotti. | + | '''Expanding Textual Entailment Corpora Fromwikipedia Using Co-Training''' - scientific work related to [[Wikipedia quality]] published in 2010, written by [[Fabio Massimo Zanzotto]] and [[Marco Pennacchiotti]]. |
== Overview == | == Overview == | ||
| − | In this paper authors propose a novel method to automatically extract large textual entailment datasets homogeneous to existing ones. The key idea is the combination of two intuitions: (1) the use of Wikipedia to extract a large set of textual entailment pairs; (2) the application of semisupervised machine learning methods to make the extracted dataset homogeneous to the existing ones. Authors report empirical evidence that method successfully expands existing textual entailment corpora. | + | In this paper authors propose a novel method to automatically extract large textual entailment datasets homogeneous to existing ones. The key idea is the combination of two intuitions: (1) the use of [[Wikipedia]] to extract a large set of textual entailment pairs; (2) the application of semisupervised machine learning methods to make the extracted dataset homogeneous to the existing ones. Authors report empirical evidence that method successfully expands existing textual entailment corpora. |
Revision as of 08:25, 2 June 2019
Expanding Textual Entailment Corpora Fromwikipedia Using Co-Training - scientific work related to Wikipedia quality published in 2010, written by Fabio Massimo Zanzotto and Marco Pennacchiotti.
Overview
In this paper authors propose a novel method to automatically extract large textual entailment datasets homogeneous to existing ones. The key idea is the combination of two intuitions: (1) the use of Wikipedia to extract a large set of textual entailment pairs; (2) the application of semisupervised machine learning methods to make the extracted dataset homogeneous to the existing ones. Authors report empirical evidence that method successfully expands existing textual entailment corpora.
