Relation extraction is one of the important research topics in the field of information extraction research. To solve the problem of semantic variation in traditional semisupervised relation extraction algorithm, this paper proposes a novel semisupervised relation extraction algorithm based on ensemble learning (LXRE). The new algorithm mainly uses two kinds of support vector machine classifier...

Relation extraction (RE) is a fundamental NLP task that aims to identify relations between some entities regarding given text. RE forms the basis for many advanced tasks, such as question answering and text summarization, thus its quality critical relevant downstream applications. However, evaluating of models non-trivial. On one hand, obtaining ground truth labels individual test inputs tediou...

Abstract This paper presents a new task of predicting the coverage text document for relation extraction (RE): Does contain many relational tuples given entity? Coverage predictions are useful in selecting best documents knowledge base construction with large input corpora. To study this problem, we present dataset 31,366 diverse 520 entities. We analyze correlation features like length, entity...

In recent years, many scholars have chosen to use word lexicons incorporate information into a model based on character input improve the performance of Chinese relation extraction (RE). For example, Li et al. proposed MG-Lattice in 2019 and achieved state-of-the-art (SOTA) results. However, st...