A Novel End-to-End Multiple Tagging Model for Knowledge Extraction

It is an emerging research topic in NLP to joint extraction of knowledge including entities and relations from unstructured text and representing them as meaningful triplets. Despite significant progresses made by recent deep neural network based solutions, these methods still confront the overlapping issue that different relational triplets may have overlapped entities in a sentence, and it is troublesome to address this issue by current solutions. In this paper, we propose a novel multiple tagging model to address the overlapping issue and extract knowledge from unstructured text. Specifically, we devise a multiple tagging scheme that transforms the problem of joint entity and relation extraction into a multiple sequence tagging problem. By using GRU as the building block for encoding-decoding, the proposed model is capable of handling the triplet overlapping problem because the decoder layer allows one entity to take part in more than one triplet. The whole network is end-to-end trianable and outputs all triplets in a sentence directly. Experimental results on the NYT and KBP benchmarks demonstrate that the proposed model siginificantly improves the recall of triplet, and consequently, achieving the new state-of-the-art in the task of triplet extraction on both datasets.

I. Introduction

Nowadays, Knowledge Graph (KG) plays an important role in many intelligent systems and it is usually a collection of relational facts. Every fact always called knowledge,is composed of entities and a semantic relation, and can be represented as a triplet: <Entity A, Relation R, Entity B>, for instance <Beijing, Country-Capital, China>. Automatic extraction of knowledge from unstructured texts is an essential step toward constructing and refining KG. The task has gained increasing attention from NLP researchers.