Cross-sentence N-ary relation classification using LSTMs on graph and sequence structures

Abstract

Relation classification is an important semantic processing task in the field of Natural Language Processing (NLP). The past works mainly focused on binary relations in a single sentence. Recently, cross-sentence N-ary relation classification, which detects relations among n entities across multiple sentences, has been arousing people’s interests. The dependency tree based methods and some Graph Neural Network (GNN) based methods have been carried out to convey rich structural information. However, it is challenging for researchers to fully use the relevant information while ignore the irrelevant information from the dependency trees. In this paper, we propose a Graph Attention-based LSTM (GA LSTM) network to make full use of the relevant graph structure information. The dependency tree of multiple sentences is divided into many subtrees whose root node is a word in the sentence and the leaf nodes are regarded as the neighborhood. A graph attention mechanism is used to aggregate the local information in the neighborhood. Using this network, we identify the relevant information from the dependency tree. On the other hand, because the GNNs highly depend on the graph structure of the sentence and lack context sequence structural information, their effectiveness to the task is limited. To tackle this problem, we propose an N-gram Graph LSTM (NGG LSTM) network, which updates the hidden states by aggregating graph neighbor node information and the inherent sequence structural information of sentence. The experimental results show that our methods outperform most of the existing methods.