Unsupervised Relation Extraction

Many errors produced by unsupervised and semi-supervised relation extraction (RE) systems occur because of wrong recogni- tion of entities that participate in the rela- tions. This is especially true for …...

Unsupervised Relation Extraction (URE) is the task of extracting relations of a priori unknown semantic types using clustering methods on a vector space model of entity pairs and patterns. In …...

A common approach to unsupervised relation extraction builds clusters of patterns express- ing the same relation. In order to obtain clus- ters of relational patterns of good quality, we have …...

AbstractUnsupervised relation extraction aims at extracting relations between entities in text. Previous unsupervised approaches are either generative or discriminative. In a supervised setting, discriminative approaches, such as deep neural network …...

AbstractUnsupervised relation extraction (URE) extracts relations between named entities from raw text without manually-labelled data and existing knowledge bases (KBs). URE methods can be categorised into generative and discriminative approaches, …...

AbstractWe show that state-of-the-art self-supervised language models can be readily used to extract relations from a corpus without the need to train a fine-tuned extractive head. We introduce RE-Flex, a …...

AbstractUnsupervised relation extraction aims to extract the relationship between entities from natural language sentences without prior information on relational scope or distribution. Existing works either utilize self-supervised schemes to refine …...