Leveraging 2-hop Distant Supervision from Table Entity Pairs for Relation Extraction

Abstract

Distant supervision (DS) has been widely used to automatically construct (noisy) labeled data for relation extraction (RE). Given two entities, distant supervision exploits sentences that directly mention them for predicting their semantic relation. We refer to this strategy as 1-hop DS, which unfortunately may not work well for long-tail entities with few supporting sentences. In this paper, we introduce a new strategy named 2-hop DS to enhance distantly supervised RE, based on the observation that there exist a large number of relational tables on the Web which contain entity pairs that share common relations. We refer to such entity pairs as anchors for each other, and collect all sentences that mention the anchor entity pairs of a given target entity pair to help relation prediction. We develop a new neural RE method REDS2 in the multi-instance learning paradigm, which adopts a hierarchical model structure to fuse information respectively from 1-hop DS and 2-hop DS. Extensive experimental results on a benchmark dataset show that REDS2 can consistently outperform various baselines across different settings by a substantial margin. 1

Improving Distant Supervised Relation Extraction with Noise Detection Strategy

“…Specifically, in the proposed framework: 1) To learn a comprehensive sentence representation for each sentence in corpus and web tables (high-quality relational tables extracted from http://websail-fe.cs.northwestern.edu/TabEL/#content-code (access date: 19 April 2013) [10], we firstly combine the multi-head self-attention mechanism and piecewise convolution neural network (PCNN) for the sentence encoder; 2) In this stage, we use a noise detection strategy to address the issue of noisy labeling. To evaluate the correlation of sentences and labels, we calculate the similarity between entity-aware embeddings and each sentence representation.…”

“…Beltagy et al [9] combined distant supervision with a directly supervised data and used it to improve weights of relevant sentences. Deng et al [10] proposed a hierarchical framework to fuse information from DS and web tables that share relational facts about entities to further improve RE.…”

“…In addition, we used the WikiTable corpus dataset aligned with the NYT. The dataset and more details on how to build it were provided by Deng et al [10]. In this paper, NYT and WikiTable corpus are denoted as S 1 h,t and S 2 h,t , respectively.…”

“…To demonstrate the effectiveness of the NDRE, we set most hyper-parameters in the NDRE following previous work [10,14] and adjusted other hyper-parameters using cross-validation on the training data.…”

“…At present, many studies have applied deep learning in DS and improved distant supervision relation extraction (DSRE) by automatically learning text features [4][5][6]. In addition, some scholars have found that the addition of additional knowledge can further improve DSRE, thus gaining great attention [7][8][9][10]. Among them, Deng et al [10] proposed a hierarchical framework to fuse information from DS and web tables that share entity relationship facts, which greatly improves the efficiency of DSRE.…”