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Entropy 2016, 18(6), 204;

Distant Supervision for Relation Extraction with Ranking-Based Methods

Intelligence Computing Research Center, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen 518055, China
This paper is an extended version of our paper published in the 22nd International Conference on Neural Information Processing, Istanbul, Turkey, 9–12 November 2015.
Authors to whom correspondence should be addressed.
Academic Editor: Raúl Alcaraz Martínez
Received: 18 February 2016 / Revised: 12 May 2016 / Accepted: 18 May 2016 / Published: 24 May 2016
(This article belongs to the Section Information Theory)
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Relation extraction has benefited from distant supervision in recent years with the development of natural language processing techniques and data explosion. However, distant supervision is still greatly limited by the quality of training data, due to its natural motivation for greatly reducing the heavy cost of data annotation. In this paper, we construct an architecture called MIML-sort (Multi-instance Multi-label Learning with Sorting Strategies), which is built on the famous MIML framework. Based on MIML-sort, we propose three ranking-based methods for sample selection with which we identify relation extractors from a subset of the training data. Experiments are set up on the KBP (Knowledge Base Propagation) corpus, one of the benchmark datasets for distant supervision, which is large and noisy. Compared with previous work, the proposed methods produce considerably better results. Furthermore, the three methods together achieve the best F1 on the official testing set, with an optimal enhancement of F1 from 27.3% to 29.98%. View Full-Text
Keywords: distant supervision; relation extraction; multi-instance multi-label learning; ranking distant supervision; relation extraction; multi-instance multi-label learning; ranking

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Xiang, Y.; Chen, Q.; Wang, X.; Qin, Y. Distant Supervision for Relation Extraction with Ranking-Based Methods. Entropy 2016, 18, 204.

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