Distant Supervision for Relation Extraction via Piecewise Convolutional Neural Networks

Abstract

Two problems arise when using distant supervision for relation extraction. First, in this method, an already existing knowledge base is heuristically aligned to texts, and the alignment results are treated as labeled data. However, the heuristic alignment can fail, resulting in wrong label problem. In addition, in previous approaches, statistical models have typically been applied to ad hoc features. The noise that originates from the feature extraction process can cause poor performance.In this paper, we propose a novel model dubbed the Piecewise Convolutional Neural Networks (PCNNs) with multi-instance learning to address these two problems. To solve the first problem, distant supervised relation extraction is treated as a multi-instance problem in which the uncertainty of instance labels is taken into account. To address the latter problem, we avoid feature engineering and instead adopt convolutional architecture with piecewise max pooling to automatically learn relevant features. Experiments show that our method is effective and outperforms several competitive baseline methods.

Context-Aware Representations for Knowledge Base Relation Extraction

“…Mintz et al (2009) and Riedel et al (2010) have used manually engineered features based on part-of-speech tags and dependency parses to represent the target relations. Recently, Zeng et al (2015) and have shown that one can successfully apply convo-lutional neural networks to extract sentence-level features automatically.…”

“…Most of the methods (Riedel et al, 2010;Zeng et al, 2015;Lin et al, 2016) focus on predicting a single relation type based on the combined evidence from all of the occurrences of an entity pair. Hoffmann et al (2011) and Surdeanu et al (2012) assign multiple relation types to each entity pair, such that the predictions are tied to particular occurrences of the entity pair.…”

Adversarial Training for Relation Extraction

“…We first introduce MIML and then describe the base neural network models we consider: 1 piecewise CNN (Zeng et al, 2015) (PCNN) and bidirectional GRU (Cho et al, 2014) (RNN). We also utilize the selective attention mechanism in for both PCNN and RNN models.…”

Relation extraction with weakly supervised learning based on process-structure-property-performance reciprocity

“…Residual learning is used to help the deep CNN network [12]. Zeng et al [13] split a sentence into three parts, and then applied max pooling to each part of the sentence over a CNN layer.…”