{"title":"Reducing Feature Embedding Data for Discovering Relations in Big Text Data","authors":"Haojie Huang, R. Wong","doi":"10.1109/BigDataCongress.2019.00038","DOIUrl":null,"url":null,"abstract":"Relation extraction is a critical task in building a knowledge base from unstructured text documents. Most works in automatic relation extraction have applied deep learning techniques such as Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) in large text corpora. However, they require a large amount of human labelling data, which is labour intensive and is hardly applied in a new domain of document without human supervision. This paper proposes a novel framework to extract relations in multi-domain texts effectively. In particular, we construct the framework in three phases including preprocessing, feature embedding and relation extraction. We show that a small proportion of training data is sufficient to train our relation extraction framework and achieve a good accuracy in relation extraction works.","PeriodicalId":335850,"journal":{"name":"2019 IEEE International Congress on Big Data (BigDataCongress)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE International Congress on Big Data (BigDataCongress)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BigDataCongress.2019.00038","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
Relation extraction is a critical task in building a knowledge base from unstructured text documents. Most works in automatic relation extraction have applied deep learning techniques such as Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) in large text corpora. However, they require a large amount of human labelling data, which is labour intensive and is hardly applied in a new domain of document without human supervision. This paper proposes a novel framework to extract relations in multi-domain texts effectively. In particular, we construct the framework in three phases including preprocessing, feature embedding and relation extraction. We show that a small proportion of training data is sufficient to train our relation extraction framework and achieve a good accuracy in relation extraction works.