Log in

Relevant bibliographies by topics / Relation extraction / Journal articles

To see the other types of publications on this topic, follow the link: Relation extraction.

Journal articles on the topic 'Relation extraction'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'Relation extraction.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

Asgari-Bidhendi, Majid, Mehrdad Nasser, Behrooz Janfada, and Behrouz Minaei-Bidgoli. "PERLEX: A Bilingual Persian-English Gold Dataset for Relation Extraction." Scientific Programming 2021 (March 16, 2021): 1–8. http://dx.doi.org/10.1155/2021/8893270.

Full text

Abstract:

Relation extraction is the task of extracting semantic relations between entities in a sentence. It is an essential part of some natural language processing tasks such as information extraction, knowledge extraction, question answering, and knowledge base population. The main motivations of this research stem from a lack of a dataset for relation extraction in the Persian language as well as the necessity of extracting knowledge from the growing big data in the Persian language for different applications. In this paper, we present “PERLEX” as the first Persian dataset for relation extraction,

APA, Harvard, Vancouver, ISO, and other styles

2

AlArfaj, Abeer. "Towards relation extraction from Arabic text: a review." International Robotics & Automation Journal 5, no. 5 (2019): 212–15. http://dx.doi.org/10.15406/iratj.2019.05.00195.

Full text

Abstract:

Semantic relation extraction is an important component of ontologies that can support many applications e.g. text mining, question answering, and information extraction. However, extracting semantic relations between concepts is not trivial and one of the main challenges in Natural Language Processing (NLP) Field. The Arabic language has complex morphological, grammatical, and semantic aspects since it is a highly inflectional and derivational language, which makes task even more challenging. In this paper, we present a review of the state of the art for relation extraction from texts, address

APA, Harvard, Vancouver, ISO, and other styles

3

Zakria, Gehad, Mamdouh Farouk, Khaled Fathy, and Malak N. Makar. "Relation Extraction from Arabic Wikipedia." Indian Journal of Science and Technology 12, no. 46 (2019): 01–06. http://dx.doi.org/10.17485/ijst/2019/v12i46/147512.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Kong, Lingqi, and Shengquau Liu. "REACT: Relation Extraction Method Based on Entity Attention Network and Cascade Binary Tagging Framework." Applied Sciences 14, no. 7 (2024): 2981. http://dx.doi.org/10.3390/app14072981.

Full text

Abstract:

With the development of the Internet, vast amounts of text information are being generated constantly. Methods for extracting the valuable parts from this information have become an important research field. Relation extraction aims to identify entities and the relations between them from text, helping computers better understand textual information. Currently, the field of relation extraction faces various challenges, particularly in addressing the relation overlapping problem. The main difficulties are as follows: (1) Traditional methods of relation extraction have limitations and lack the a

APA, Harvard, Vancouver, ISO, and other styles

5

Peng, Nanyun, Hoifung Poon, Chris Quirk, Kristina Toutanova, and Wen-tau Yih. "Cross-Sentence N-ary Relation Extraction with Graph LSTMs." Transactions of the Association for Computational Linguistics 5 (December 2017): 101–15. http://dx.doi.org/10.1162/tacl_a_00049.

Full text

Abstract:

Past work in relation extraction has focused on binary relations in single sentences. Recent NLP inroads in high-value domains have sparked interest in the more general setting of extracting n-ary relations that span multiple sentences. In this paper, we explore a general relation extraction framework based on graph long short-term memory networks (graph LSTMs) that can be easily extended to cross-sentence n-ary relation extraction. The graph formulation provides a unified way of exploring different LSTM approaches and incorporating various intra-sentential and inter-sentential dependencies, s

APA, Harvard, Vancouver, ISO, and other styles

6

Park, Seongsik, and Harksoo Kim. "Dual Pointer Network for Fast Extraction of Multiple Relations in a Sentence." Applied Sciences 10, no. 11 (2020): 3851. http://dx.doi.org/10.3390/app10113851.

Full text

Abstract:

Relation extraction is a type of information extraction task that recognizes semantic relationships between entities in a sentence. Many previous studies have focused on extracting only one semantic relation between two entities in a single sentence. However, multiple entities in a sentence are associated through various relations. To address this issue, we proposed a relation extraction model based on a dual pointer network with a multi-head attention mechanism. The proposed model finds n-to-1 subject–object relations using a forward object decoder. Then, it finds 1-to-n subject–object relati

APA, Harvard, Vancouver, ISO, and other styles

7

Zhang, Xiaoliang, Feng Gao, Lunsheng Zhou, et al. "Fine-Grained Drug Interaction Extraction Based on Entity Pair Calibration and Pre-Training Model for Chinese Drug Instructions." International Journal on Semantic Web and Information Systems 18, no. 1 (2022): 1–23. http://dx.doi.org/10.4018/ijswis.307908.

Full text

Abstract:

Existing pharmaceutical information extraction research often focus on standalone entity or relationship identification tasks over drug instructions. There is a lack of a holistic solution for drug knowledge extraction. Moreover, current methods perform poorly in extracting fine-grained interaction relations from drug instructions. To solve these problems, this paper proposes an information extraction framework for drug instructions. The framework proposes deep learning models with fine-tuned pre-training models for entity recognition and relation extraction, in addition, it incorporates an no

APA, Harvard, Vancouver, ISO, and other styles

8

Zhou, Deyu, Dayou Zhong, and Yulan He. "Biomedical Relation Extraction: From Binary to Complex." Computational and Mathematical Methods in Medicine 2014 (2014): 1–18. http://dx.doi.org/10.1155/2014/298473.

Full text

Abstract:

Biomedical relation extraction aims to uncover high-quality relations from life science literature with high accuracy and efficiency. Early biomedical relation extraction tasks focused on capturing binary relations, such as protein-protein interactions, which are crucial for virtually every process in a living cell. Information about these interactions provides the foundations for new therapeutic approaches. In recent years, more interests have been shifted to the extraction of complex relations such as biomolecular events. While complex relations go beyond binary relations and involve more th

APA, Harvard, Vancouver, ISO, and other styles

9

Lai, Qinghan, Zihan Zhou, and Song Liu. "Joint Entity-Relation Extraction via Improved Graph Attention Networks." Symmetry 12, no. 10 (2020): 1746. http://dx.doi.org/10.3390/sym12101746.

Full text

Abstract:

Joint named entity recognition and relation extraction is an essential natural language processing task that aims to identify entities and extract the corresponding relations in an end-to-end manner. At present, compared with the named entity recognition task, the relation extraction task performs poorly on complex text. To solve this problem, we proposed a novel joint model named extracting Entity-Relations viaImproved Graph Attention networks (ERIGAT), which enhances the ability of the relation extraction task. In our proposed model, we introduced the graph attention network to extract entit

APA, Harvard, Vancouver, ISO, and other styles

10

Zhang, Qinghui, Meng Wu, Pengtao Lv, Mengya Zhang, and Lei Lv. "Research on Chinese Medical Entity Relation Extraction Based on Syntactic Dependency Structure Information." Applied Sciences 12, no. 19 (2022): 9781. http://dx.doi.org/10.3390/app12199781.

Full text

Abstract:

Extracting entity relations from unstructured medical texts is a fundamental task in the field of medical information extraction. In relation extraction, dependency trees contain rich structural information that helps capture the long-range relations between entities. However, many models cannot effectively use dependency information or learn sentence information adequately. In this paper, we propose a relation extraction model based on syntactic dependency structure information. First, the model learns sentence sequence information by Bi-LSTM. Then, the model learns syntactic dependency struc

APA, Harvard, Vancouver, ISO, and other styles

11

Chen, Mei, Tingting Zhang, and Shibin Wang. "Prompting large language models to extract chemical‒disease relation precisely and comprehensively at the document level: an evaluation study." PLOS ONE 20, no. 4 (2025): e0320123. https://doi.org/10.1371/journal.pone.0320123.

Full text

Abstract:

Given the scarcity of annotated data, current deep learning methods face challenges in the field of document-level chemical-disease relation extraction, making it difficult to achieve precise relation extraction capable of identifying relation types and comprehensive extraction tasks that identify relation-related factors. This study tests the abilities of three large language models (LLMs), GPT3.5, GPT4.0, and Claude-opus, to perform precise and comprehensive extraction in document-level chemical-disease relation extraction on a self-constructed dataset. Firstly, based on the task characteris

APA, Harvard, Vancouver, ISO, and other styles

12

Takanobu, Ryuichi, Tianyang Zhang, Jiexi Liu, and Minlie Huang. "A Hierarchical Framework for Relation Extraction with Reinforcement Learning." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 7072–79. http://dx.doi.org/10.1609/aaai.v33i01.33017072.

Full text

Abstract:

Most existing methods determine relation types only after all the entities have been recognized, thus the interaction between relation types and entity mentions is not fully modeled. This paper presents a novel paradigm to deal with relation extraction by regarding the related entities as the arguments of a relation. We apply a hierarchical reinforcement learning (HRL) framework in this paradigm to enhance the interaction between entity mentions and relation types. The whole extraction process is decomposed into a hierarchy of two-level RL policies for relation detection and entity extraction

APA, Harvard, Vancouver, ISO, and other styles

13

HACHEY, B., C. GROVER, and R. TOBIN. "Datasets for generic relation extraction." Natural Language Engineering 18, no. 1 (2011): 21–59. http://dx.doi.org/10.1017/s1351324911000106.

Full text

Abstract:

AbstractA vast amount of usable electronic data is in the form of unstructured text. The relation extraction task aims to identify useful information in text (e.g. PersonW works for OrganisationX, GeneY encodes ProteinZ) and recode it in a format such as a relational database or RDF triplestore that can be more effectively used for querying and automated reasoning. A number of resources have been developed for training and evaluating automatic systems for relation extraction in different domains. However, comparative evaluation is impeded by the fact that these corpora use different markup for

APA, Harvard, Vancouver, ISO, and other styles

14

Liu, Zishu, Yongquan Liang, and Weijian Ni. "Document-Level Causal Event Extraction Enhanced by Temporal Relations Using Dual-Channel Neural Network." Electronics 14, no. 5 (2025): 992. https://doi.org/10.3390/electronics14050992.

Full text

Abstract:

Event–event causal relation extraction (ECRE) represents a critical yet challenging task in natural language processing. Existing studies primarily focus on extracting causal sentences and events, despite the use of joint extraction methods for both tasks. However, both pipeline methods and joint extraction approaches often overlook the impact of document-level event temporal sequences on causal relations. To address this limitation, we propose a model that incorporates document-level event temporal order information to enhance the extraction of implicit causal relations between events. The pr

APA, Harvard, Vancouver, ISO, and other styles

15

Han, Dongchen, Zhaoqian Zheng, Hui Zhao, Shanshan Feng, and Haiting Pang. "Span-based single-stage joint entity-relation extraction model." PLOS ONE 18, no. 2 (2023): e0281055. http://dx.doi.org/10.1371/journal.pone.0281055.

Full text

Abstract:

Extracting entities and relations from the unstructured text has attracted increasing attention in recent years. The existing work has achieved considerable results, yet it is difficult to solve entity overlap and exposure bias. To address cascading errors, exposure bias, and entity overlap in existing entity relation extraction approaches, we propose a joint entity relation extraction model (SMHS) based on a span-level multi-head selection mechanism, transforming entity relation extraction into a span-level multi-head selection problem. Our model uses span-tagger and span-embedding to constru

APA, Harvard, Vancouver, ISO, and other styles

16

Huynh, Nghia Huu, Quoc Bao Ho, and Te An Nguyen. "An approach in health relation extraction." Science & Technology Development Journal - Economics - Law and Management 1, Q3 (2017): 51–63. http://dx.doi.org/10.32508/stdjelm.v1iq3.449.

Full text

Abstract:

Extracting relations among medical concepts is very important in the medical field. The relations denote the events or the possible relations between the concepts. Information about these relations provides users with a full view of medical problems. This helps physicians and health-care practitioners make effective decisions and minimize errors in the treatment process. This paper collects methods for relations extraction in health texts and presents an approach on one type of specific relation (i.e. template filling). The approach combines methods including rule-based and machine learningbas

APA, Harvard, Vancouver, ISO, and other styles

17

Li, Dao Wang. "Research on Text Conceptual Relation Extraction Based on Domain Ontology." Advanced Materials Research 739 (August 2013): 574–79. http://dx.doi.org/10.4028/www.scientific.net/amr.739.574.

Full text

Abstract:

At present, the ontology learning research focuses on the concept and relation extraction; the traditional extraction methods ignore the influence of the semantic factors on the extraction results, and lack of the accurate extraction of the relations among concepts. According to this problem, in this paper, the association rule is combined with the semantic similarity, and the improved comprehensive semantic similarity is applied into the relation extraction through the association rule mining relation. The experiments show that the relation extraction based on this method effectively improves

APA, Harvard, Vancouver, ISO, and other styles

18

Zhu, Zhen, Huaiyuan Lin, Dongmei Gu, Liting Wang, Hong Wu, and Yun Fang. "MusREL." International Journal on Semantic Web and Information Systems 19, no. 1 (2023): 1–19. http://dx.doi.org/10.4018/ijswis.329965.

Full text

Abstract:

In order to enhance the utility of online educational digital resources, the authors propose a practical and efficient multi-strategy relation extraction (RE) model in online education scenarios. First, the effective relation discrimination model is used to make relation predictions for non-structured teaching resources and eliminate the noise data. Then, they extract relations from different path strategies using multiple low-computational resources and efficient relation extraction strategies and use their proposed multi-strategy weighting calculator to weigh the relation extraction strategi

APA, Harvard, Vancouver, ISO, and other styles

19

Halike, Ayiguli, Kahaerjiang Abiderexiti, and Tuergen Yibulayin. "Semi-Automatic Corpus Expansion and Extraction of Uyghur-Named Entities and Relations Based on a Hybrid Method." Information 11, no. 1 (2020): 31. http://dx.doi.org/10.3390/info11010031.

Full text

Abstract:

Relation extraction is an important task with many applications in natural language processing, such as structured knowledge extraction, knowledge graph construction, and automatic question answering system construction. However, relatively little past work has focused on the construction of the corpus and extraction of Uyghur-named entity relations, resulting in a very limited availability of relation extraction research and a deficiency of annotated relation data. This issue is addressed in the present article by proposing a hybrid Uyghur-named entity relation extraction method that combines

APA, Harvard, Vancouver, ISO, and other styles

20

Yu, Ning, Jianyi Liu, and Yu Shi. "Span-Based Fine-Grained Entity-Relation Extraction via Sub-Prompts Combination." Applied Sciences 13, no. 2 (2023): 1159. http://dx.doi.org/10.3390/app13021159.

Full text

Abstract:

With the development of information extraction technology, a variety of entity-relation extraction paradigms have been formed. However, approaches guided by these existing paradigms suffer from insufficient information fusion and too coarse extraction granularity, leading to difficulties extracting all triples in a sentence. Moreover, the joint entity-relation extraction model cannot easily adapt to the relation extraction task. Therefore, we need to design more fine-grained and flexible extraction methods. In this paper, we propose a new extraction paradigm based on existing paradigms. Then,

APA, Harvard, Vancouver, ISO, and other styles

21

Knez, Timotej, Miha Štravs, and Slavko Žitnik. "Semi-Supervised Relation Extraction Corpus Construction and Models Creation for Under-Resourced Languages: A Use Case for Slovene." Information 16, no. 2 (2025): 143. https://doi.org/10.3390/info16020143.

Full text

Abstract:

The goal of relation extraction is to recognize head and tail entities in a document and determine a relation between them. While a lot of progress was made in solving automated relation extraction in widely used languages such as English, the use of these methods for under-resourced languages and domains is limited due to the lack of training data. In this work, we present a pipeline using distant supervision for constructing a relation extraction corpus in an arbitrary language. The corpus construction combines Wikipedia documents in the target language with relations in the WikiData knowled

APA, Harvard, Vancouver, ISO, and other styles

22

Rukaiya, Habib, and Musfique Anwar Md. "Finding out Noisy Patterns for Relation Extraction of Bangla Sentences." International Journal on Natural Language Computing (IJNLC) 9, no. 1 (2023): 12. https://doi.org/10.5281/zenodo.7896151.

Full text

Abstract:

Relation extraction is one of the most important parts of natural language processing. It is the process of extracting relationships from a text. Extracted relationships actually occur between two or more entities of a certain type and these relations may have different patterns. The goal of the paper is to find out the noisy patterns for relation extraction of Bangla sentences. For the research work, seed tuples were needed containing two entities and the relation between them. We can get seed tuples from Freebase. Freebase is a large collaborative knowledge base and database of general, stru

APA, Harvard, Vancouver, ISO, and other styles

23

Rukaiya, Habib, and Musfique Anwar Md. "FINDING OUT NOISY PATTERNS FOR RELATION EXTRACTION OF BANGLA SENTENCES." International Journal on Natural Language Computing (IJNLC) 9, no. 1 (2022): 12. https://doi.org/10.5281/zenodo.7260964.

Full text

Abstract:

Relation extraction is one of the most important parts of natural language processing. It is the process of extracting relationships from a text. Extracted relationships actually occur between two or more entities of a certain type and these relations may have different patterns. The goal of the paper is to find out the noisy patterns for relation extraction of Bangla sentences. For the research work, seed tuples were needed containing two entities and the relation between them. We can get seed tuples from Freebase. Freebase is a large collaborative knowledge base and database of general, stru

APA, Harvard, Vancouver, ISO, and other styles

24

Tian, Wentao, Zheng Wang, Yuqian Fu, Jingjing Chen, and Lechao Cheng. "Open-Vocabulary Video Relation Extraction." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 6 (2024): 5215–23. http://dx.doi.org/10.1609/aaai.v38i6.28328.

Full text

Abstract:

A comprehensive understanding of videos is inseparable from describing the action with its contextual action-object interactions. However, many current video understanding tasks prioritize general action classification and overlook the actors and relationships that shape the nature of the action, resulting in a superficial understanding of the action. Motivated by this, we introduce Open-vocabulary Video Relation Extraction (OVRE), a novel task that views action understanding through the lens of action-centric relation triplets. OVRE focuses on pairwise relations that take part in the action a

APA, Harvard, Vancouver, ISO, and other styles

25

Christopoulou, Fenia, Thy Thy Tran, Sunil Kumar Sahu, Makoto Miwa, and Sophia Ananiadou. "Adverse drug events and medication relation extraction in electronic health records with ensemble deep learning methods." Journal of the American Medical Informatics Association 27, no. 1 (2019): 39–46. http://dx.doi.org/10.1093/jamia/ocz101.

Full text

Abstract:

AbstractObjectiveIdentification of drugs, associated medication entities, and interactions among them are crucial to prevent unwanted effects of drug therapy, known as adverse drug events. This article describes our participation to the n2c2 shared-task in extracting relations between medication-related entities in electronic health records.Materials and MethodsWe proposed an ensemble approach for relation extraction and classification between drugs and medication-related entities. We incorporated state-of-the-art named-entity recognition (NER) models based on bidirectional long short-term mem

APA, Harvard, Vancouver, ISO, and other styles

26

Zhang, Qian-Wen, Zhao Yan, Tianyang Zhao, et al. "MMKE: A Multi-Model Knowledge Extraction System from Unstructured Texts." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 18 (2021): 16124–26. http://dx.doi.org/10.1609/aaai.v35i18.18032.

Full text

Abstract:

In this work, we present a Multi-Model Knowledge Extraction (MMKE) System which consists of two unstructured text extraction models (RelationSO model and SubjectRO model) based on a multi-task learning framework. Instead of recognizing entity first and then predicting relationships between entity pairs in previous works, MMKE detects subject and corresponding relationships before extracting objects to cope with the diverse object-type problem, overlapping problem and non-predefined relation problem. Our system accepts unstructured text as input, from which it automatically extracts triplets kn

APA, Harvard, Vancouver, ISO, and other styles

27

Dai, Dai, Xinyan Xiao, Yajuan Lyu, Shan Dou, Qiaoqiao She, and Haifeng Wang. "Joint Extraction of Entities and Overlapping Relations Using Position-Attentive Sequence Labeling." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 6300–6308. http://dx.doi.org/10.1609/aaai.v33i01.33016300.

Full text

Abstract:

Joint entity and relation extraction is to detect entity and relation using a single model. In this paper, we present a novel unified joint extraction model which directly tags entity and relation labels according to a query word position p, i.e., detecting entity at p, and identifying entities at other positions that have relationship with the former. To this end, we first design a tagging scheme to generate n tag sequences for an n-word sentence. Then a position-attention mechanism is introduced to produce different sentence representations for every query position to model these n tag seque

APA, Harvard, Vancouver, ISO, and other styles

28

Gao, Chuhan, Guixian Xu, and Yueting Meng. "Integrated Extraction of Entities and Relations via Attentive Graph Convolutional Networks." Electronics 13, no. 22 (2024): 4373. http://dx.doi.org/10.3390/electronics13224373.

Full text

Abstract:

For information security, entity and relation extraction can be applied in sensitive information protection, data leakage detection, and other aspects. The current approaches to entity relation extraction not only ignore the relevance and dependency between name entity recognition and relation extraction but also may result in the cumulative propagation of errors. To solve this problem, it is proposed that an end-to-end joint entity and relation extraction model based on the Attention mechanism and Graph Convolutional Network (GCN) to simultaneously extract named entities and their relationshi

APA, Harvard, Vancouver, ISO, and other styles

29

Wang, Jian, Xiaoyu Chen, Yu Zhang, et al. "Document-Level Biomedical Relation Extraction Using Graph Convolutional Network and Multihead Attention: Algorithm Development and Validation." JMIR Medical Informatics 8, no. 7 (2020): e17638. http://dx.doi.org/10.2196/17638.

Full text

Abstract:

Background Automatically extracting relations between chemicals and diseases plays an important role in biomedical text mining. Chemical-disease relation (CDR) extraction aims at extracting complex semantic relationships between entities in documents, which contain intrasentence and intersentence relations. Most previous methods did not consider dependency syntactic information across the sentences, which are very valuable for the relations extraction task, in particular, for extracting the intersentence relations accurately. Objective In this paper, we propose a novel end-to-end neural networ

APA, Harvard, Vancouver, ISO, and other styles

30

Gill, Jaskaran, Madhu Chetty, Suryani Lim, and Jennifer Hallinan. "Knowledge-Based Intelligent Text Simplification for Biological Relation Extraction." Informatics 10, no. 4 (2023): 89. http://dx.doi.org/10.3390/informatics10040089.

Full text

Abstract:

Relation extraction from biological publications plays a pivotal role in accelerating scientific discovery and advancing medical research. While vast amounts of this knowledge is stored within the published literature, extracting it manually from this continually growing volume of documents is becoming increasingly arduous. Recently, attention has been focused towards automatically extracting such knowledge using pre-trained Large Language Models (LLM) and deep-learning algorithms for automated relation extraction. However, the complex syntactic structure of biological sentences, with nested e

APA, Harvard, Vancouver, ISO, and other styles

31

Vo, Duc-Thuan, and Ebrahim Bagheri. "Open information extraction." Encyclopedia with Semantic Computing and Robotic Intelligence 01, no. 01 (2017): 1630003. http://dx.doi.org/10.1142/s2425038416300032.

Full text

Abstract:

Open information extraction (Open IE) systems aim to obtain relation tuples with highly scalable extraction in portable across domain by identifying a variety of relation phrases and their arguments in arbitrary sentences. The first generation of Open IE learns linear chain models based on unlexicalized features such as Part-of-Speech (POS) or shallow tags to label the intermediate words between pair of potential arguments for identifying extractable relations. Open IE currently is developed in the second generation that is able to extract instances of the most frequently observed relation typ

APA, Harvard, Vancouver, ISO, and other styles

32

Vukic-Culafic, Branka, and Djordje Petrovic. "Possibility of determining relations between translatory movement and inclination of agonist teeth after extraction of first permanent molars." Medical review 67, suppl. 2 (2014): 61–65. http://dx.doi.org/10.2298/mpns14s2061v.

Full text

Abstract:

Introduction. Tooth extractions, particularly of the permanent teeth, lead to different and numerous consequences. It is known that the incidence of caries and the incidence of premature extractions are the highest with the first permanent molars. The aims of this study were to examine the possibility of determining the relations between the translatory movement and the agonist teeth inclination depending on the time of the performed extraction, as well as on the temporal distance from the moment of extracting the first permanent molar to the moment of analyzing the changes. Material and Metho

APA, Harvard, Vancouver, ISO, and other styles

33

Shi, Xue, Yingping Yi, Ying Xiong, et al. "Extracting entities with attributes in clinical text via joint deep learning." Journal of the American Medical Informatics Association 26, no. 12 (2019): 1584–91. http://dx.doi.org/10.1093/jamia/ocz158.

Full text

Abstract:

Abstract Objective Extracting clinical entities and their attributes is a fundamental task of natural language processing (NLP) in the medical domain. This task is typically recognized as 2 sequential subtasks in a pipeline, clinical entity or attribute recognition followed by entity-attribute relation extraction. One problem of pipeline methods is that errors from entity recognition are unavoidably passed to relation extraction. We propose a novel joint deep learning method to recognize clinical entities or attributes and extract entity-attribute relations simultaneously. Materials and Method

APA, Harvard, Vancouver, ISO, and other styles

34

Xuan, Zhaoxin, Hejing Zhao, Xin Li, and Ziqi Chen. "Distantly Supervised Relation Extraction Method Based on Multi-Level Hierarchical Attention." Information 16, no. 5 (2025): 364. https://doi.org/10.3390/info16050364.

Full text

Abstract:

Distantly Supervised Relation Extraction (DSRE) aims to automatically identify semantic relationships within large text corpora by aligning with external knowledge bases. Despite the success of current methods in automating data annotation, they introduce two main challenges: label noise and data long-tail distribution. Label noise results in inaccurate annotations, which can undermine the quality of relation extraction. The long-tail problem, on the other hand, leads to an imbalanced model that struggles to extract less frequent, long-tail relations. In this paper, we introduce a novel relati

APA, Harvard, Vancouver, ISO, and other styles

35

Jiang, Bo, and Jia Cao. "Joint Extraction of Entities and Relations via Entity and Relation Heterogeneous Graph Attention Networks." Applied Sciences 13, no. 2 (2023): 842. http://dx.doi.org/10.3390/app13020842.

Full text

Abstract:

Entity and relation extraction (ERE) is a core task in information extraction. This task has always faced the overlap problem. It was found that heterogeneous graph attention networks could enhance semantic analysis and fusion between entities and relations to improve the ERE performance in our previous work. In this paper, an entity and relation heterogeneous graph attention network (ERHGA) is proposed for joint ERE. A heterogeneous graph attention network with a gate mechanism was constructed containing word nodes, subject nodes, and relation nodes to learn and enhance the embedding of parts

APA, Harvard, Vancouver, ISO, and other styles

36

Liu, Shuang, XunQin Chen, Peng Chen, and Simon Kolmanič. "Label-Guided relation prototype generation for Continual Relation Extraction." PeerJ Computer Science 10 (October 8, 2024): e2327. http://dx.doi.org/10.7717/peerj-cs.2327.

Full text

Abstract:

Continual relation extraction (CRE) aims to extract relations towards the continuous and iterative arrival of new data. To address the problem of catastrophic forgetting, some existing research endeavors have focused on exploring memory replay methods by storing typical historical learned instances or embedding all observed relations as prototypes by averaging the hidden representation of samples and replaying them in the subsequent training process. However, this prototype generation method overlooks the rich semantic information within the label namespace and are also constrained by memory s

APA, Harvard, Vancouver, ISO, and other styles

37

Wu, Shangjia, Zhiqiang Guo, Xiaofeng Huang, Jialiang Zhang, and Yingfang Ni. "SPECE: Subject Position Encoder in Complex Embedding for Relation Extraction." Electronics 13, no. 13 (2024): 2571. http://dx.doi.org/10.3390/electronics13132571.

Full text

Abstract:

As a crucial component of many natural language processing tasks, extracting entities and relations transforms unstructured text information into structured data, providing essential support for constructing knowledge graphs (KGs). However, current entity relation extraction models often prioritize the extraction of richer semantic features or the optimization of relation extraction methods, overlooking the significance of positional information and subject characteristics in this task. To solve this problem, we introduce the subject position-based complex exponential embedding for entity rela

APA, Harvard, Vancouver, ISO, and other styles

38

Thuy, Nguyen Thi Thanh, Nguyen Ngoc Diep, Ngo Xuan Bach, and Tu Minh Phuong. "Joint Reference and Relation Extraction from Legal Documents with Enhanced Decoder Input." Cybernetics and Information Technologies 23, no. 2 (2023): 72–86. http://dx.doi.org/10.2478/cait-2023-0014.

Full text

Abstract:

Abstract This paper deals with an important task in legal text processing, namely reference and relation extraction from legal documents, which includes two subtasks: 1) reference extraction; 2) relation determination. Motivated by the fact that two subtasks are related and share common information, we propose a joint learning model that solves simultaneously both subtasks. Our model employs a Transformer-based encoder-decoder architecture with non-autoregressive decoding that allows relaxing the sequentiality of traditional seq2seq models and extracting references and relations in one inferen

APA, Harvard, Vancouver, ISO, and other styles

39

Postal, Paul M. "Contrasting extraction types." Journal of Linguistics 30, no. 1 (1994): 159–86. http://dx.doi.org/10.1017/s0022226700016212.

Full text

Abstract:

This paper grounds a novel typology yielding three major types of English (L(eft)-extraction, defined by their relation to resumptive pronouns (RPs): (1) B-extractions, which require RPs in their extraction sites, (2) A1-extractions, which allow RPs in their extraction sites, and (3) A2-extractions, which forbid RPs in their extraction sites. Type B is represented by topicalization; type A1 by most instances of question extraction. The A/B distinction is supported by correlations with restrictions on definite pronouns. A2-extractions, e.g. free relative extraction, are insensitive to such and

APA, Harvard, Vancouver, ISO, and other styles

40

Min, Bonan, Shuming Shi, Ralph Grishman, and Chin-Yew Lin. "Towards Large-Scale Unsupervised Relation Extraction from the Web." International Journal on Semantic Web and Information Systems 8, no. 3 (2012): 1–23. http://dx.doi.org/10.4018/jswis.2012070101.

Full text

Abstract:

The Web brings an open-ended set of semantic relations. Discovering the significant types is very challenging. Unsupervised algorithms have been developed to extract relations from a corpus without knowing the relation types in advance, but most rely on tagging arguments of predefined types. One recently reported system is able to jointly extract relations and their argument semantic classes, taking a set of relation instances extracted by an open IE (Information Extraction) algorithm as input. However, it cannot handle polysemy of relation phrases and fails to group many similar (“synonymous”

APA, Harvard, Vancouver, ISO, and other styles

41

Wu, Tongtong, Xuekai Li, Yuan-Fang Li, et al. "Curriculum-Meta Learning for Order-Robust Continual Relation Extraction." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 12 (2021): 10363–69. http://dx.doi.org/10.1609/aaai.v35i12.17241.

Full text

Abstract:

Continual relation extraction is an important task that focuses on extracting new facts incrementally from unstructured text. Given the sequential arrival order of the relations, this task is prone to two serious challenges, namely catastrophic forgetting and order-sensitivity. We propose a novel curriculum-meta learning method to tackle the above two challenges in continual relation extraction. We combine meta learning and curriculum learning to quickly adapt model parameters to a new task and to reduce interference of previously seen tasks on the current task. We design a novel relation repr

APA, Harvard, Vancouver, ISO, and other styles

42

Liu, Bin, Jialin Tao, Wanyuan Chen, et al. "Integration of Relation Filtering and Multi-Task Learning in GlobalPointer for Entity and Relation Extraction." Applied Sciences 14, no. 15 (2024): 6832. http://dx.doi.org/10.3390/app14156832.

Full text

Abstract:

The rise of knowledge graphs has been instrumental in advancing artificial intelligence (AI) research. Extracting entity and relation triples from unstructured text is crucial for the construction of knowledge graphs. However, Chinese text has a complex grammatical structure, which may lead to the problem of overlapping entities. Previous pipeline models have struggled to address such overlap problems effectively, while joint models require entity annotations for each predefined relation in the set, which results in redundant relations. In addition, the traditional models often lead to task im

APA, Harvard, Vancouver, ISO, and other styles

43

Zhang, Xinsong, Pengshuai Li, Weijia Jia, and Hai Zhao. "Multi-Labeled Relation Extraction with Attentive Capsule Network." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 7484–91. http://dx.doi.org/10.1609/aaai.v33i01.33017484.

Full text

Abstract:

To disclose overlapped multiple relations from a sentence still keeps challenging. Most current works in terms of neural models inconveniently assuming that each sentence is explicitly mapped to a relation label, cannot handle multiple relations properly as the overlapped features of the relations are either ignored or very difficult to identify. To tackle with the new issue, we propose a novel approach for multi-labeled relation extraction with capsule network which acts considerably better than current convolutional or recurrent net in identifying the highly overlapped relations within an in

APA, Harvard, Vancouver, ISO, and other styles

44

Yuan, Changsen, Heyan Huang, and Chong Feng. "Multi-Graph Cooperative Learning Towards Distant Supervised Relation Extraction." ACM Transactions on Intelligent Systems and Technology 12, no. 5 (2021): 1–21. http://dx.doi.org/10.1145/3466560.

Full text

Abstract:

The Graph Convolutional Network (GCN) is a universal relation extraction method that can predict relations of entity pairs by capturing sentences’ syntactic features. However, existing GCN methods often use dependency parsing to generate graph matrices and learn syntactic features. The quality of the dependency parsing will directly affect the accuracy of the graph matrix and change the whole GCN’s performance. Because of the influence of noisy words and sentence length in the distant supervised dataset, using dependency parsing on sentences causes errors and leads to unreliable information. T

APA, Harvard, Vancouver, ISO, and other styles

45

Liu, Xiaofeng, Jianye Fan, and Shoubin Dong. "Document-Level Biomedical Relation Extraction Leveraging Pretrained Self-Attention Structure and Entity Replacement: Algorithm and Pretreatment Method Validation Study." JMIR Medical Informatics 8, no. 5 (2020): e17644. http://dx.doi.org/10.2196/17644.

Full text

Abstract:

Background The most current methods applied for intrasentence relation extraction in the biomedical literature are inadequate for document-level relation extraction, in which the relationship may cross sentence boundaries. Hence, some approaches have been proposed to extract relations by splitting the document-level datasets through heuristic rules and learning methods. However, these approaches may introduce additional noise and do not really solve the problem of intersentence relation extraction. It is challenging to avoid noise and extract cross-sentence relations. Objective This study aime

APA, Harvard, Vancouver, ISO, and other styles

46

Zhao, Tianyang, Zhao Yan, Yunbo Cao, and Zhoujun Li. "A Unified Multi-Task Learning Framework for Joint Extraction of Entities and Relations." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 16 (2021): 14524–31. http://dx.doi.org/10.1609/aaai.v35i16.17707.

Full text

Abstract:

Joint extraction of entities and relations focuses on detecting entity pairs and their relations simultaneously with a unified model. Based on the extraction order, previous works mainly solve this task through relation-last, relation-first and relation-middle manner. However, these methods still suffer from the template-dependency, non-entity detection and non-predefined relation prediction problem. To overcome these challenges, in this paper, we propose a unified multi-task learning framework to divide the task into three interacted sub-tasks. Specifically, we first introduce the type-attent

APA, Harvard, Vancouver, ISO, and other styles

47

Chen, Yang, and Bowen Shi. "Enhanced Heterogeneous Graph Attention Network with a Novel Multilabel Focal Loss for Document-Level Relation Extraction." Entropy 26, no. 3 (2024): 210. http://dx.doi.org/10.3390/e26030210.

Full text

Abstract:

Recent years have seen a rise in interest in document-level relation extraction, which is defined as extracting all relations between entities in multiple sentences of a document. Typically, there are multiple mentions corresponding to a single entity in this context. Previous research predominantly employed a holistic representation for each entity to predict relations, but this approach often overlooks valuable information contained in fine-grained entity mentions. We contend that relation prediction and inference should be grounded in specific entity mentions rather than abstract entity con

APA, Harvard, Vancouver, ISO, and other styles

48

Long, Jun, Lei Liu, Hongxiao Fei, et al. "Contextual Semantic-Guided Entity-Centric GCN for Relation Extraction." Mathematics 10, no. 8 (2022): 1344. http://dx.doi.org/10.3390/math10081344.

Full text

Abstract:

Relation extraction tasks aim to predict potential relations between entities in a target sentence. As entity mentions have ambiguity in sentences, some important contextual information can guide the semantic representation of entity mentions to improve the accuracy of relation extraction. However, most existing relation extraction models ignore the semantic guidance of contextual information to entity mentions and treat entity mentions in and the textual context of a sentence equally. This results in low-accuracy relation extractions. To address this problem, we propose a contextual semantic-

APA, Harvard, Vancouver, ISO, and other styles

49

Gill, Jaskaran Kaur, Madhu Chetty, Suryani Lim, and Jennifer Hallinan. "Large language model based framework for automated extraction of genetic interactions from unstructured data." PLOS ONE 19, no. 5 (2024): e0303231. http://dx.doi.org/10.1371/journal.pone.0303231.

Full text

Abstract:

Extracting biological interactions from published literature helps us understand complex biological systems, accelerate research, and support decision-making in drug or treatment development. Despite efforts to automate the extraction of biological relations using text mining tools and machine learning pipelines, manual curation continues to serve as the gold standard. However, the rapidly increasing volume of literature pertaining to biological relations poses challenges in its manual curation and refinement. These challenges are further compounded because only a small fraction of the publish

APA, Harvard, Vancouver, ISO, and other styles

50

Li, Huagang, and Bo Liu. "An Open Relation Extraction System for Web Text Information." Applied Sciences 12, no. 11 (2022): 5718. http://dx.doi.org/10.3390/app12115718.

Full text

Abstract:

Web texts typically undergo the open-ended growth of new relations. Traditional relation extraction methods lack automatic annotation and perform poorly on new relation extraction tasks. We propose an open-domain relation extraction system (ORES) based on distant supervision and few-shot learning to solve this problem. More specifically, we utilize tBERT to design instance selector 1, implementing automatic labeling in the data mining component. Meanwhile, we design example selector 2 based on K-BERT in the new relation extraction component. The real-time data management component outputs new

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!