Relation classification is an important NLP task to extract relations between entities. The
state-of-the-art methods for relation classification are primarily based on Convolutional or …

Relation classification is a crucial ingredient in numerous information extraction systems
seeking to mine structured facts from text. We propose a novel convolutional neural network …

Convolutional Neural Networks (CNNs) have recently achieved remarkably strong
performance on the practically important task of sentence classification (kim 2014 …

One long-term goal of machine learning research is to produce methods that are applicable
to reasoning and natural language, in particular building an intelligent dialogue agent. To …

Relation classification is an important research arena in the field of natural language
processing (NLP). In this paper, we present SDP-LSTM, a novel neural network to classify …

Relation classification is an important semantic processing, which has achieved great
attention in recent years. The main challenge is the fact that important information can …

Knowledge graph completion (KGC) can predict missing links and is crucial for real-world
knowledge graphs, which widely suffer from incompleteness. KGC methods assume a …

Relation classification is an important semantic processing task for which state-ofthe-art
systems still rely on costly handcrafted features. In this work we tackle the relation …

Up to now, relation extraction systems have made extensive use of features generated by
linguistic analysis modules. Errors in these features lead to errors of relation detection and …

Deep learning has gained much success in sentence-level relation classification. For
example, convolutional neural networks (CNN) have delivered competitive performance …