Relevant bibliographies by topics / Relation extraction

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Relation extraction'

Author: Grafiati
Published: 4 June 2021
Last updated: 26 July 2025

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Journal articles on the topic "Relation extraction"

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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.

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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,
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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.

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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
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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.

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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.

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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
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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.

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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
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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.

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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
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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.

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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
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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.

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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
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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.

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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
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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.

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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
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Dissertations / Theses on the topic "Relation extraction"

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Hachey, Benjamin. "Towards generic relation extraction." Thesis, University of Edinburgh, 2009. http://hdl.handle.net/1842/3978.

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A 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 that can be more effectively used for querying and automated reasoning. However, adapting conventional relation extraction systems to new domains or tasks requires significant effort from annotators and developers. Furthermore, previous adaptation approaches based on bootstrapping start from example instances of the target relat
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Augenstein, Isabelle. "Web relation extraction with distant supervision." Thesis, University of Sheffield, 2016. http://etheses.whiterose.ac.uk/13247/.

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Being able to find relevant information about prominent entities quickly is the main reason to use a search engine. However, with large quantities of information on the World Wide Web, real time search over billions of Web pages can waste resources and the end user’s time. One of the solutions to this is to store the answer to frequently asked general knowledge queries, such as the albums released by a musical artist, in a more accessible format, a knowledge base. Knowledge bases can be created and maintained automatically by using information extraction methods, particularly methods to extrac
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Simon, Etienne. "Deep Learning for Unsupervised Relation Extraction." Electronic Thesis or Diss., Sorbonne université, 2022. http://www.theses.fr/2022SORUS198.

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Détecter les relations exprimées dans un texte est un problème fondamental de la compréhension du langage naturel. Il constitue un pont entre deux approches historiquement distinctes de l'intelligence artificielle, celles à base de représentations symboliques et distribuées. Cependant, aborder ce problème sans supervision humaine pose plusieurs problèmes et les modèles non supervisés ont des difficultés à faire écho aux avancées des modèles supervisés. Cette thèse aborde deux lacunes des approches non supervisées : le problème de la régularisation des modèles discriminatifs et le problème d'ex
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Yeh, Hui-Syuan. "Prompt-based Relation Extraction for Pharmacovigilance." Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASG097.

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L'extraction de connaissances à jour à partir de sources textuelles diverses est importante pour la santé publique. Alors que les sources professionnelles, notamment les revues scientifiques et les notes cliniques, fournissent les connaissances les plus fiables, les observations apportées dans les forums de patients et les médias sociaux permettent d'obtenir des informations complémentaires pour certains thèmes. Détecter les entités et leurs relations dans ces sources variées est particulièrement précieux. Nous nous concentrons sur l'extraction de relations dans le domaine médical. Nous commen
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Afzal, Naveed. "Unsupervised relation extraction for e-learning applications." Thesis, University of Wolverhampton, 2011. http://hdl.handle.net/2436/299064.

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In this modern era many educational institutes and business organisations are adopting the e-Learning approach as it provides an effective method for educating and testing their students and staff. The continuous development in the area of information technology and increasing use of the internet has resulted in a huge global market and rapid growth for e-Learning. Multiple Choice Tests (MCTs) are a popular form of assessment and are quite frequently used by many e-Learning applications as they are well adapted to assessing factual, conceptual and procedural information. In this thesis, we pre
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Imani, Mahsa. "Evaluating open relation extraction over conversational texts." Thesis, University of British Columbia, 2014. http://hdl.handle.net/2429/45978.

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In this thesis, for the first time the performance of Open IE systems on conversational data has been studied. Due to lack of test datasets in this domain, a method for creating the test dataset covering a wide range of conversational data has been proposed. Conversational text is more complex and challenging for relation extraction because of its cryptic content and ungrammatical colloquial language. As a consequence text simplification has been used as a remedy to empower Open IE tools for relation extraction. Experimental results show that text simplification helps OLLIE, a state of the art
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Loper, Edward (Edward Daniel) 1977. "Applying semantic relation extraction to information retrieval." Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/86521.

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Minard, Anne-Lyse. "Extraction de relations en domaine de spécialité." Phd thesis, Université Paris Sud - Paris XI, 2012. http://tel.archives-ouvertes.fr/tel-00777749.

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La quantité d'information disponible dans le domaine biomédical ne cesse d'augmenter. Pour que cette information soit facilement utilisable par les experts d'un domaine, il est nécessaire de l'extraire et de la structurer. Pour avoir des données structurées, il convient de détecter les relations existantes entre les entités dans les textes. Nos recherches se sont focalisées sur la question de l'extraction de relations complexes représentant des résultats expérimentaux, et sur la détection et la catégorisation de relations binaires entre des entités biomédicales. Nous nous sommes intéressée aux
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Zhang, Shaomin. "Thematic knowledge extraction." Thesis, Nottingham Trent University, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.272437.

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Bai, Fan. "Structured Minimally Supervised Learning for Neural Relation Extraction." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu159666392917093.

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Books on the topic "Relation extraction"

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Xu, Fei-Yu. Bootstrapping relation extraction from semantic seeds. German Research Center for Artificial Intelligence, 2008.

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Dinh, Ly. Text-Based Network Construction of Crisis Response Networks Using Entity and Relation Extraction. SAGE Publications Ltd, 2025. https://doi.org/10.4135/9781036217280.

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Hudson, R. A. Extraction and grammatical relations. The author, 1987.

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Tonelli, Paolo, ed. Il giudizio nell'estrazione del terzo molare inferiore. Firenze University Press, 2022. http://dx.doi.org/10.36253/978-88-5518-576-9.

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The scientific literature and clinical dental practice have in the extraction of the lower third molar an area of wide interest. In fact, parallel to the presence of prejudices and false beliefs on the part of patients, there is also a great variability of scientific opinions among professionals in the field, both in relation to the indications for extraction, diagnostic aids, and intra- and post-operative management. Our text aims to merge the awareness given by clinical experience with the knowledge of an evidence-based dental culture, proposing itself as a stimulus for in-depth study for st
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Barlow, Alfred E. Report on the origin, geological relations and composition of the nickel and copper deposits of the Sudbury mining district, Ontario, Canada. S.E. Dawson, 1997.

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Priol, Florence Le. Extraction et capitalisation automatiques de connaissances à partir de documents textuels: Seek-java : identification et interprétation de relations entre concepts. A.N.R.T, Université de Lille III, 2000.

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Botero, Luz Victoria Calle. La familia: Extracto de su historia, estructura, funciones, elementos esenciales y problemática jurídica. Editorial Kelly, 1985.

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1805-1880, Jackson Charles T., ed. Review of "Reports on the geological relations, chemical analysis, and microscopic examination of the coal of the Albert Coal Mining Company, situated in Hillsboro', Albert County, New Brunswick ": As written and compiled by Charles T. Jackson, M.D., of Boston. s.n.], 1985.

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Petrucci, Alessandra, and Rosanna Verde, eds. SIS 2017. Statistics and Data Science: new challenges, new generations. Firenze University Press, 2017. http://dx.doi.org/10.36253/978-88-6453-521-0.

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The 2017 SIS Conference aims to highlight the crucial role of the Statistics in Data Science. In this new domain of ‘meaning’ extracted from the data, the increasing amount of produced and available data in databases, nowadays, has brought new challenges. That involves different fields of statistics, machine learning, information and computer science, optimization, pattern recognition. These afford together a considerable contribute in the analysis of ‘Big data’, open data, relational and complex data, structured and no-structured. The interest is to collect the contributes which provide from
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Fagbore, Adam S., Nabhojeet Sen, and Katherine Roscoe. Punishment, Labour and the Legitimation of Power. Amsterdam University Press, 2025. https://doi.org/10.5117/9789463724777.

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This volume draws the outlines of a new field of scholarship at the crossroads of the social histories of punishment and labour. It poses key questions: What is “punishment” and how is it legitimized? In particular, how do punitive practices contribute to shape the processes of labour extraction and workers’ mobility? Based on empirically grounded research on a wide range of geographical and temporal contexts, this volume provides important insights on these questions and on the ways through which they can be studied. It highlights the need to pluralize both punishment and labour, moving beyon
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Book chapters on the topic "Relation extraction"

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Denecke, Kerstin. "Relation Extraction." In Health Web Science. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20582-3_9.

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Castelli, Vittorio, and Imed Zitouni. "Relation Extraction." In Natural Language Processing of Semitic Languages. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-45358-8_9.

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Devarakonda, Murthy V., Kalpana Raja, and Hua Xu. "Relation Extraction." In Cognitive Informatics in Biomedicine and Healthcare. Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-55865-8_5.

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Rossiello, Gaetano, Alfio Gliozzo, Nicolas Fauceglia, and Giovanni Semeraro. "Latent Relational Model for Relation Extraction." In The Semantic Web. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-21348-0_19.

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Cai, Hua, Qing Xu, and Weilin Shen. "Complex Relative Position Encoding for Improving Joint Extraction of Entities and Relations." In Proceeding of 2021 International Conference on Wireless Communications, Networking and Applications. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2456-9_66.

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AbstractRelative position encoding (RPE) is important for transformer based pretrained language model to capture sequence ordering of input tokens. Transformer based model can detect entity pairs along with their relation for joint extraction of entities and relations. However, prior works suffer from the redundant entity pairs, or ignore the important inner structure in the process of extracting entities and relations. To address these limitations, in this paper, we first use BERT with complex relative position encoding (cRPE) to encode the input text information, then decompose the joint ext
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Barrière, Caroline. "Pattern-Based Relation Extraction." In Natural Language Understanding in a Semantic Web Context. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41337-2_11.

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Sen, Seckin, and Ilyas Cicekli. "Weakly Supervised Relation Extraction." In Innovative Methods in Computer Science and Computational Applications in the Era of Industry 5.0. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-56322-5_9.

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Elsahar, Hady, Elena Demidova, Simon Gottschalk, Christophe Gravier, and Frederique Laforest. "Unsupervised Open Relation Extraction." In Lecture Notes in Computer Science. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70407-4_3.

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Soni, Ameet, Dileep Viswanathan, Jude Shavlik, and Sriraam Natarajan. "Learning Relational Dependency Networks for Relation Extraction." In Inductive Logic Programming. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-63342-8_7.

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Cui, Meiji, Li Li, Zhihong Wang, and Mingyu You. "A Survey on Relation Extraction." In Communications in Computer and Information Science. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-7359-5_6.

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Conference papers on the topic "Relation extraction"

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Stickley, Daniel. "Relating Relations: Meta-Relation Extraction from Online Health Forum Posts." In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop. Association for Computational Linguistics, 2021. http://dx.doi.org/10.18653/v1/2021.eacl-srw.18.

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Yu, Junjie, Tong Zhu, Wenliang Chen, Wei Zhang, and Min Zhang. "Improving Relation Extraction with Relational Paraphrase Sentences." In Proceedings of the 28th International Conference on Computational Linguistics. International Committee on Computational Linguistics, 2020. http://dx.doi.org/10.18653/v1/2020.coling-main.148.

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Cong, Xin, Jiawei Sheng, Shiyao Cui, Bowen Yu, Tingwen Liu, and Bin Wang. "Relation-Guided Few-Shot Relational Triple Extraction." In SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 2022. http://dx.doi.org/10.1145/3477495.3531831.

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Yu, Junjie, Tong Zhu, Wenliang Chen, Wei Zhang, and Min Zhang. "Improving Relation Extraction with Relational Paraphrase Sentences." In Proceedings of the 28th International Conference on Computational Linguistics. International Committee on Computational Linguistics, 2020. http://dx.doi.org/10.18653/v1/2020.coling-main.148.

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Su, Yu, Honglei Liu, Semih Yavuz, Izzeddin Gur, Huan Sun, and Xifeng Yan. "Global Relation Embedding for Relation Extraction." In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). Association for Computational Linguistics, 2018. http://dx.doi.org/10.18653/v1/n18-1075.

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Long, Xinwei, Shuzi Niu, and Yucheng Li. "Consistent Inference for Dialogue Relation Extraction." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/535.

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Relation Extraction is key to many downstream tasks. Dialogue relation extraction aims at discovering entity relations from multi-turn dialogue scenario. There exist utterance, topic and relation discrepancy mainly due to multi-speakers, utterances, and relations. In this paper, we propose a consistent learning and inference method to minimize possible contradictions from those distinctions. First, we design mask mechanisms to refine utterance-aware and speaker-aware representations respectively from the global dialogue representation for the utterance distinction. Then a gate mechanism is pro
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Yu, Bowen, Zhenyu Zhang, Tingwen Liu, Bin Wang, Sujian Li, and Quangang Li. "Beyond Word Attention: Using Segment Attention in Neural Relation Extraction." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/750.

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Relation extraction studies the issue of predicting semantic relations between pairs of entities in sentences. Attention mechanisms are often used in this task to alleviate the inner-sentence noise by performing soft selections of words independently. Based on the observation that information pertinent to relations is usually contained within segments (continuous words in a sentence), it is possible to make use of this phenomenon for better extraction. In this paper, we aim to incorporate such segment information into neural relation extractor. Our approach views the attention mechanism as lin
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Chen, Xiaoyu, and Rohan Badlani. "Relation Extraction with Contextualized Relation Embedding (CRE)." In Proceedings of Deep Learning Inside Out (DeeLIO): The First Workshop on Knowledge Extraction and Integration for Deep Learning Architectures. Association for Computational Linguistics, 2020. http://dx.doi.org/10.18653/v1/2020.deelio-1.2.

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Kuang, Jun, Yixin Cao, Jianbing Zheng, Xiangnan He, Ming Gao, and Aoying Zhou. "Improving Neural Relation Extraction with Implicit Mutual Relations." In 2020 IEEE 36th International Conference on Data Engineering (ICDE). IEEE, 2020. http://dx.doi.org/10.1109/icde48307.2020.00093.

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Yang, Huifan, Da-Wei Li, Zekun Li, Donglin Yang, and Bin Wu. "Open Relation Extraction with Non-existent and Multi-span Relationships." In 19th International Conference on Principles of Knowledge Representation and Reasoning {KR-2022}. International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/kr.2022/37.

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Open relation extraction (ORE) aims to assign semantic relationships among arguments, essential to the automatic construction of knowledge graphs (KG). The previous ORE methods and some benchmark datasets consider a relation between two arguments as definitely existing and in a simple single-span form, neglecting possible non-existent relationships and flexible, expressive multi-span relations. However, detecting non-existent relations is necessary for a pipelined information extraction system (first performing named entity recognition then relation extraction), and multi-span relationships co
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Reports on the topic "Relation extraction"

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Do, Quang X. Background Knowledge in Learning-Based Relation Extraction. Defense Technical Information Center, 2012. http://dx.doi.org/10.21236/ada565270.

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Ward, Katrina, Jonathan Bisila, and Kelsey Cairns. Survey of Current State of the Art Entity-Relation Extraction Tools. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1630263.

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Ward, Katrina, Jonathan Bisila, and Kelsey Cairns. Survey of Current State of the Art Entity-Relation Extraction Tools. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1662019.

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Dorr, Bonnie, and Terry Gaasterland. Summarization-Inspired Temporal-Relation Extraction: Tense-Pair Templates and Treebank-3 Analysis. Defense Technical Information Center, 2006. http://dx.doi.org/10.21236/ada460392.

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Ma, Yue, and Felix Distel. Learning Formal Definitions for Snomed CT from Text. Technische Universität Dresden, 2013. http://dx.doi.org/10.25368/2022.193.

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Snomed CT is a widely used medical ontology which is formally expressed in a fragment of the Description Logic EL++. The underlying logics allow for expressive querying, yet make it costly to maintain and extend the ontology. Existing approaches for ontology generation mostly focus on learning superclass or subclass relations and therefore fail to be used to generate Snomed CT definitions. In this paper, we present an approach for the extraction of Snomed CT definitions from natural language texts, based on the distance relation extraction approach. By benefiting from a relatively large amount
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Diehl, Rebecca, Jonathan Friedman, Rebecca Diehl, and Jonathan Friedman. Modelling effects of flow withdrawal scenarios on riverine and riparian features of the Yampa River in Dinosaur National Monument. National Park Service, 2024. http://dx.doi.org/10.36967/2305338.

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The National Park Service (NPS) is charged with maintaining natural riverine resources and processes in its parks along the Yampa River and downstream along the Green River. This mission requires information on how proposed water withdrawals would affect resources. We present a methodology that quantifies the impact on natural riverine and riparian features of Dinosaur National Monument based on alternative withdrawals that vary in volume and timing. This methodology uses a reverse quantification and develops tools to enable the NPS to ensure that if withdrawals must occur, the adverse impacts
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7

Kjelvik, Grete. Dementia prevention in the Nordics. Nordic Welfare Centre, 2024. http://dx.doi.org/10.52746/yoln7568.

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The expected increase in the number of people suffering from dementia is intertwined with the ageing Nordic population. More and more older adults will live with dementia diseases impacting their everyday lives.The Nordic societies are trying to adjust to this challenge and to the rising needs of good dementia care. In parallel, mounting evidence on the efficacy of dementia prevention encourages the Nordic countries to upgrade their preventive work, and to mitigate the effects of cognitive decline in the population.This report explores dementia prevention in the Nordics and provides examples o
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Adam, Gaelen P., Melinda Davies, Jerusha George, et al. Machine Learning Tools To (Semi-) Automate Evidence Synthesis. Agency for Healthcare Research and Quality (AHRQ), 2025. https://doi.org/10.23970/ahrqepcwhitepapermachine.

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Introduction. Tools that leverage machine learning, a subset of artificial intelligence, are becoming increasingly important for conducting evidence synthesis as the volume and complexity of primary literature expands exponentially. In response, we have created a living rapid review and evidence map to understand existing research and identify available tools. Methods. We searched PubMed, Embase, and the ACM Digital Library from January 1, 2021, to April 3, 2024, for comparative studies, and identified older studies using the reference lists of existing evidence synthesis products (ESPs). We p
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Canavire-Bacarreza, Gustavo, Jorge Martínez-Vázquez, and Cristián Sepúlveda. Sub-national Revenue Mobilization in Peru. Inter-American Development Bank, 2012. http://dx.doi.org/10.18235/0011368.

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This paper analyzes the problem of sub-national revenue mobilization in Peru and proposes several policy reforms to improve collection performance while maintaining a sound revenue structure. In particular, the paper analyzes the current revenues of regional and municipal governments and identifies the main priorities for reform. Among the most important problems are the acute inequalities and inefficiencies associated with revenue sharing from extractive industries. These revenues represent a significant share of sub-national budgets and currently they are distributed without consideration of
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Börjesson, Patrik, Maria Eggertsen, Lachlan Fetterplace, et al. Long-term effects of no-take zones in Swedish waters. Edited by Ulf Bergström, Charlotte Berkström, and Mattias Sköld. Department of Aquatic Resources, Swedish University of Agricultural Sciences, 2023. http://dx.doi.org/10.54612/a.10da2mgf51.

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Marine protected areas (MPAs) are increasingly established worldwide to protect and restore degraded ecosystems. However, the level of protection varies among MPAs and has been found to affect the outcome of the closure. In no-take zones (NTZs), no fishing or extraction of marine organisms is allowed. The EU Commission recently committed to protect 30% of European waters by 2030 through the updated Biodiversity Strategy. Importantly, one third of these 30% should be of strict protection. Exactly what is meant by strict protection is not entirely clear, but fishing would likely have to be fully
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