[Retracted] Utilizing Entity‐Based Gated Convolution and Multilevel Sentence Attention to Improve Distantly Supervised Relation Extraction

Abstract

Distant supervision is an effective method to automatically collect large-scale datasets for relation extraction (RE). Automatically constructed datasets usually comprise two types of noise: the intrasentence noise and the wrongly labeled noisy sentence. To address issues caused by the above two types of noise and improve distantly supervised relation extraction, this paper proposes a novel distantly supervised relation extraction model, which consists of an entity-based gated convolution sentence encoder and a multilevel sentence selective attention (Matt) module. Specifically, we first apply an entity-based gated convolution operation to force the sentence encoder to extract entity-pair-related features and filter out useless intrasentence noise information. Furthermore, the multilevel attention schema fuses the bag information to obtain a fine-grained bag-specific query vector, which can better identify valid sentences and reduce the influence of wrongly labeled sentences. Experimental results on a large-scale benchmark dataset show that our model can effectively reduce the influence of the above two types of noise and achieves state-of-the-art performance in relation extraction.

Movie Scene Event Extraction with Graph Attention Network Based on Argument Correlation Information

“…Natural language processing (NLP) technology is widely used in media analysis [ 13 , 14 , 15 , 16 ]. As an essential task of NLP, event extraction can be divided into two subtasks: (1) event trigger extraction, in which the “trigger” (that represents the occurrence of an event) is extracted and then assigned an event type, and (2) argument extraction, in which the arguments of the trigger are detected, and then each argument is assigned an argument role with respect to the event type.…”

Retracted: Utilizing Entity‐Based Gated Convolution and Multilevel Sentence Attention to Improve Distantly Supervised Relation Extraction

“…This article has been retracted by Hindawi, as publisher, following an investigation undertaken by the publisher [ 1 ]. This investigation has uncovered evidence of systematic manipulation of the publication and peer-review process.…”