Representation Learning for Weakly Supervised Relation Extraction

arXiv:2105.00815v3 Announce Type: replace
Abstract: Recent years have seen rapid development in Information Extraction, as well as its subtask, Relation Extraction. Relation Extraction is able to detect semantic relations between entities in sentences. Currently, many efficient approaches have been applied to relation extraction tasks. Supervised learning approaches especially have good performance. However, there are still many difficult challenges. One of the most serious problems is that manually labeled data is difficult to acquire. In most cases, limited data for supervised …

abstract arxiv cs.cl development extraction good however information information extraction performance relations representation representation learning semantic supervised learning tasks type