relation extraction
215238860
More Data, More Relations, More Context and More Openness: A Review and Outlook for Relation Extraction
Relational facts are an important component of human knowledge, which are hidden in vast amounts of text. In order to extract these facts from text, people have been working on relation extraction (RE) for years. From early pattern matching to current neural networks, existing RE methods have achieved significant progress. Yet with explosion of Web text and emergence of new relations, human knowledge is increasing drastically, and we thus require "more" from RE: a more powerful RE system that can robustly utilize more data, efficiently learn more relations, easily handle more complicated context, and flexibly generalize to more open domains. In this paper, we look back at existing RE methods, analyze key challenges we are facing nowadays, and show promising directions towards more powerful RE. We hope our view can advance this field and inspire more efforts in the community.
AACL
Xu Han Tianyu Gao Yankai Lin Hao Peng Yaoliang Yang Chaojun Xiao Zhiyuan Liu Peng Li Maosong Sun Jie Zhou
219965358
Biomedical Relation Extraction Using Distant Supervision
With the accelerating growth of big data, especially in the healthcare area, information extraction is more needed currently than ever, for it can convey unstructured information into an easily interpretable structured data. Relation extraction is the second of the two important tasks of relation extraction. This study presents an overview of relation extraction using distant supervision, providing a generalized architecture of this task based on the state of the art work that proposed this method. Besides, it surveys the methods used in the literature targeting this topic with a description of different knowledge bases used in the process along with the corpora, which can be helpful for beginner practitioners seeking knowledge on this subject. Moreover, the limitations of the proposed approaches and future challenges were highlighted, and possible solutions were proposed.
Sci. Program.
Nada Boudjellal Huaping Zhang Asif Khan Arshad Ahmad
224975392
A survey on neural relation extraction
Relation extraction is a key task for knowledge graph construction and natural language processing, which aims to extract meaningful relational information between entities from plain texts. With the development of deep learning, many neural relation extraction models were proposed recently. This paper introduces a survey on the task of neural relation extraction, including task description, widely used evaluation datasets, metrics, typical methods, challenges and recent research progresses. We mainly focus on four recent research problems: (1) how to learn the semantic representations from the given sentences for the target relation, (2) how to train a neural relation extraction model based on insufficient labeled instances, (3) how to extract relations across sentences or in a document and (4) how to jointly extract relations and corresponding entities? Finally, we give out our conclusion and future research issues.
Kang Liu
220403417
Neural relation extraction: a survey
Neural relation extraction discovers semantic relations between entities from unstructured text using deep learning methods. In this study, we present a comprehensive review of methods on neural network based relation extraction. We discuss advantageous and incompetent sides of existing studies and investigate additional research directions and improvement ideas in this field.
ArXiv
Mehmet Aydar Ozge Bozal Furkan Ozbay
222117619
Relation Extraction Using Distant Supervision: a Survey
Relation extraction is a subtask of information extraction where semantic relationships are extracted from natural language text and then classified. In essence, it allows to acquire structured knowledge from unstructured text. In this work, we present a survey of relation extraction methods that leverage pre existing structured or semi structured data to guide the extraction process. We introduce a taxonomy of existing methods and describe distant supervision approaches in detail. We describe in addition the evaluation methodologies and the datasets commonly used for quality assessment. Finally, we give a high level outlook on the field, highlighting open problems as well as the most promising research directions.
eXascale Infolab
234729143
Review of Entity Relation Extraction Methods
Ling Dongmei Zhang Yang Liu Dongyuan Li Danqiong
59337495
Relation Extraction Using Distant Supervision
Relation extraction is a subtask of information extraction where semantic relationships are extracted from natural language text and then classified. In essence, it allows us to acquire structured knowledge from unstructured text. In this article, we present a survey of relation extraction methods that leverage pre existing structured or semi structured data to guide the extraction process. We introduce a taxonomy of existing methods and describe distant supervision approaches in detail. We describe, in addition, the evaluation methodologies and the datasets commonly used for quality assessment. Finally, we give a high level outlook on the field, highlighting open problems as well as the most promising research directions.
ACM Comput. Surv.
Alisa Smirnova Philippe Cudre-Mauroux
184482827
A Brief Survey of Relation Extraction Based on Distant Supervision
As a core task and important part of Information ExtractionEntity Relation Extraction can realize the identification of the semantic relation between entity pairs. And it plays an important role in semantic understanding of sentences and the construction of entity knowledge base. It has the potential of employing distant supervision method, end to end model and other deep learning model with the creation of large datasets. In this review, we compare the contributions and defect of the various models that have been used for the task, to help guide the path ahead.
ICCS
Yong Shi Yang Xiao Lingfeng Niu
199448278
IberLEF 2019 Portuguese Named Entity Recognition and Relation Extraction Tasks
This work provides and overview of the Named Entity Recognition (NER) and Relation Extraction (RE) in Portuguese shared tasks in IberLEF 2019, its participant systems and results. These tasks sought to challenge Portuguese NER and RE systems by offering five new datasets for testing, two for RE and three for NER. Of these new datasets, two in particular offered a novel challenge for NER: the first composed of official police documents and the second composed of hospital's clinical notes. These cannot be published due to their sensitive nature, but the other three have been released for public use.
IberLEF@SEPLN
Sandra Collovini Joaquim Francisco Santos Neto Bernardo Scapini Consoli Juliano Terra Renata Vieira Paulo Quaresma Marlo Souza Daniela Barreiro Claro Rafael Glauber
207757546
A Survey of Relation Extraction of Knowledge Graphs
With the widespread use of big data, knowledge graph has become a new hotspot. It is used in intelligent question answering, recommendation system, map navigation and so on. Constructing a knowledge graph includes ontology construction, annotated data, relation extraction, and ontology inspection. Relation extraction is to solve the problem of entity semantic linking, which is of great significance to many natural language processing applications. Research related to relation extraction has gained momentum in recent years, necessitating a comprehensive survey to offer a bird's eye view of the current state of relation extraction. In this paper, we discuss the development process of relation extraction, and classify the relation extraction algorithms in recent years. Furthermore, we discuss deep learning, reinforcement learning, active learning and transfer learning. By analyzing the basic principles of supervised learning, unsupervised learning, semi supervised learning and distant supervision, we elucidate the characteristics of different relation extraction algorithms, and give the potential research directions in the future.
APWeb/WAIM Workshops
Aoran Li Xinmeng Wang Wenhuan Wang Anman Zhang Bohan Li