Learning Relation Ties with a Force-Directed Graph in Distant Supervised Relation Extraction

Distant Supervised Relation Extraction

Distant Supervised Relation Extraction with Wikipedia and Freebase

In this paper we discuss a new approach to extract relational data from unstructured text without the need of hand labeled data. Socalled distant supervision has the advantage that it scales large amounts of web data and therefore fulfills the requirement of current information extraction tasks. As opposed to supervised machine learning we train generic, relationand domain-independent extractor...

Effective Deep Memory Networks for Distant Supervised Relation Extraction

Distant supervised relation extraction (RE) has been an effective way of finding novel relational facts from text without labeled training data. Typically it can be formalized as a multi-instance multilabel problem. In this paper, we introduce a novel neural approach for distant supervised RE with special focus on attention mechanisms. Unlike the feature-based logistic regression model and comp...

Semi-Supervised Learning for Relation Extraction

This paper proposes a semi-supervised learning method for relation extraction. Given a small amount of labeled data and a large amount of unlabeled data, it first bootstraps a moderate number of weighted support vectors via SVM through a co-training procedure with random feature projection and then applies a label propagation (LP) algorithm via the bootstrapped support vectors. Evaluation on th...

Relation Extraction Using TBL with Distant Supervision

Supervised machine learning methods have been widely used in relation extraction that finds the relation between two named entities in a sentence. However, their disadvantages are that constructing training data is a cost and time consuming job, and the machine learning system is dependent on the domain of the training data. To overcome these disadvantages, we construct a weakly labeled data se...