PRGC : Potential Relation and Global Correspondence Based Joint Relational Triple Extraction (2021)

Abstract: Joint extraction of entities and relations from unstructured texts is a crucial task in information extraction. Recent methods achieve considerable performance but still suffer from some inherent limitations, such as redundancy of relation prediction, poor generalization of span-based extraction and inefficiency. In this paper, we decompose this task into three subtasks, Relation Judgement, Entity Extraction and Subject-object Alignment from a novel perspective and then propose a joint relational triple extraction framework based on Potential Relation and Global Correspondence (PRGC). Specifically, we design a component to predict potential relations, which constrains the following entity extraction to the predicted relation subset rather than all relations; then a relation-specific sequence tagging component is applied to handle the overlapping problem between subjects and objects; finally, a global correspondence component is designed to align the subject and object into a triple with low-complexity. Extensive experiments show that PRGC achieves state-of-the-art performance on public benchmarks with higher efficiency and delivers consistent performance gain on complex scenarios of overlapping triples. The source code has been submitted as the supplementary material and will be made publicly available after the blind review.

## introduction identifying entity mentions and their relations which are in the form of a triple (subject, relation, object) from unstructured texts is an important task in information extraction. some previous works proposed to address the task with pipelined approaches which include two steps: named entity recognition @xcite and relation prediction @xcite 2005; @xcite . recent end-to-end methods, which are based on either multi-task learning @xcite or singlestage framework @xcite , achieved promising performance and proved their effectiveness, but lacked in-depth study of the task. to better comprehend the task and advance the state of the art, we propose a novel perspective to decompose the task into three subtasks: i) relation judgement which aims to identify relations in a sentence, ii) entity extraction which aims to extract all subjects and objects in the sentence and iii) subject-object alignment which aims to align the subject-object pair into a triple. on the basis, we review two end-to-end methods in to address aforementioned issues, we propose an end-to-end framework which consists of three components: potential relation prediction, relation-specific sequence tagging and global correspondence, which fulfill the three subtasks accordingly as shown in for relation judgement, we predict potential relations by the potential relation prediction component rather than preserve all redundant relations, which reduces computational complexity and achieves better performance, especially when there are many relations in the dataset. 2 for entity extraction, we use a more robust relation-specific sequence tagging component (rel-spec sequence tagging for short) to extract subjects and objects separately, to naturally handle overlapping between subjects and objects. for subjectobject alignment, unlike tplinker which uses a relation-based token-pair matrix, we design a relation-independent global correspondence matrix to determine whether a specific subject-object pair is valid in a triple. given a sentence, prgc first predicts a subset of potential relations and a global matrix which contains the correspondence score between all subjects and objects; then performs sequence tagging to extract subjects and objects for each potential relation in parallel; finally enumerates all predicted entity pairs, which are then pruned by the global correspondence matrix. it is worth to note that the experiment (described in section 5.2.1) shows that the potential relation prediction component of prgc is overall beneficial, even though it introduces the exposure bias that is usually mentioned in prior single-stage methods to prove their advantages. experimental results show that prgc outperforms the state-of-the-art methods on public benchmarks with higher efficiency and fewer parameters. detailed experiments on complex scenarios such as various overlapping patterns, which contain the single entity overlap (seo), entity pair overlap (epo) and subject object overlap (soo) types foot_1 show that our method owns consistent advantages. the main contributions of this paper are as follows: ## related work traditionally, relational triple extraction has been studied as two separated tasks: entity extraction and relation prediction. early works @xcite apply the pipelined methods to perform relation classification between entity pairs after extracting all the entities. to establish the correlation between these two tasks, joint models have attracted much attention. prior featurebased joint models @xcite @xcite require a complicated process of feature engineering and rely on various nlp tools with cumbersome manual operations. recently, the neural network model which reduces manual involvement occupies the main part of the research. @xcite proposed a novel tagging scheme that unified the role of the entity and the relation between entities in the annotations, thus the joint extraction task was converted to a sequence labeling task but it failed to solve the overlapping problems. @xcite proposed to first extract all candidate entities, then predict the relation of every entity pair as a multihead selection problem, which shared parameters but did not decode jointly. nayak and ng (2020) employed an encoder-decoder architecture and a pointer network based decoding approach where an entire triple was generated at each time step. to handle the problems mentioned above, @xcite presented a cascade framework, which first identified all possible subjects in a sentence, then for each subject, applied span-based taggers to identify the corresponding objects based on each relation. this method leads to redundancy on relation judgement, and is not robust due to the span-based scheme on entity extraction. meanwhile, the alignment scheme of subjects and objects limits its parallelization. in order to represent the relation of triple explicitly, @xcite presented a relationspecific attention to assign different weights to the words in context under each relation, but it applied a naive heuristic nearest neighbor principle to combine the entity pairs which means the nearest subject and object entities will be combined into a triple. this is obviously not in accordance with intuition and fact. meanwhile, it is also redundant on relation judgement. the state-of-the-art method named tplinker @xcite ) employs a token pair linking scheme which performs two o(n 2 ) matrix operations for extracting entities and aligning subjects with objects under each relation of a sentence, causing extreme redundancy on relation judgement and complexity on subject-object alignment, respectively. and it also suffers from the disadvantage of span-based extraction scheme. ## method in this section, we first introduce our perspective of relational triple extraction task with a principled problem definition, then elaborate each component of the prgc model. an overview illustration of prgc is shown in figure 1 . ## conclusion in this paper, we presented a brand-new perspective and introduced a novel joint relational extraction framework based on potential relation and global correspondence, which greatly alleviates the problems of redundant relation judgement, poor generalization of span-based extraction and inefficient subject-object alignment. experimental results showed that our model achieved the stateof-the-art performance in the public datasets and successfully handled many complex scenarios with higher efficiency.