Distant Supervised Relation Extraction with Cost-Sensitive Loss

Using Cost-Sensitive Ranking Loss to Improve Distant Supervised Relation Extraction

Recently, many researchers have concentrated on using neural networks to learn features for Distant Supervised Relation Extraction (DSRE). However, these approaches generally employ a softmax classifier with cross-entropy loss, and bring the noise of artificial class NA into classification process. Moreover, the class imbalance problem is serious in the automatically labeled data, and results i...

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...

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...