Relevant bibliographies by topics / Open information extraction

Contents

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

Academic literature on the topic 'Open information extraction'

Author: Grafiati
Published: 4 June 2021
Last updated: 27 July 2025

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Journal articles on the topic "Open information extraction"

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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.

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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
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Akimov, Dmitry Aleksandrovich. "Automatic Extraction of Scientific Information from Open Access Publications." Journal of Advanced Research in Dynamical and Control Systems 51, SP3 (2020): 383–91. http://dx.doi.org/10.5373/jardcs/v12sp3/20201273.

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Mohamed Ali El-Morsy, Sally, Mahmoud Hussein, and Hamdy M. Mousa. "Arabic open information extraction system using dependency parsing." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 1 (2022): 541. http://dx.doi.org/10.11591/ijece.v12i1.pp541-551.

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<p>Arabic is a Semitic language and one of the most natural languages distinguished by the richness in morphological enunciation and derivation. This special and complex nature makes extracting information from the Arabic language difficult and always needs improvement. Open information extraction systems (OIE) have been emerged and used in different languages, especially in English. However, it has almost not been used for the Arabic language. Accordingly, this paper aims to introduce an OIE system that extracts the relation tuple from Arabic web text, exploiting Arabic dependency parsi
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Sally, Mohamed Ali El-Morsy, Hussein Mahmoud, and M. Mousa Hamdy. "Arabic open information extraction system using dependency parsing." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 1 (2022): 541–51. https://doi.org/10.11591/ijece.v12i1.pp541-551.

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Arabic is a Semitic language and one of the most natural languages distinguished by the richness in morphological enunciation and derivation. This special and complex nature makes extracting information from the Arabic language difficult and always needs improvement. Open information extraction systems (OIE) have been emerged and used in different languages, especially in English. However, it has almost not been used for the Arabic language. Accordingly, this paper aims to introduce an OIE system that extracts the relation tuple from Arabic web text, exploiting Arabic dependency parsing and th
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Hwang, Hyunsun, and Changki Lee. "BERT-based Korean Open Information Extraction." KIISE Transactions on Computing Practices 26, no. 9 (2020): 414–18. http://dx.doi.org/10.5626/ktcp.2020.26.9.414.

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Etzioni, Oren, Michele Banko, Stephen Soderland, and Daniel S. Weld. "Open information extraction from the web." Communications of the ACM 51, no. 12 (2008): 68–74. http://dx.doi.org/10.1145/1409360.1409378.

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Bassa, Akim, Mark Kroll, and Roman Kern. "GerIE - An Open Information Extraction System for the German Language." JUCS - Journal of Universal Computer Science 24, no. (1) (2018): 2–24. https://doi.org/10.3217/jucs-024-01-0002.

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Open Information Extraction (OIE) allows to extract relations from a text without the need of domain-speci_c training data. To date, most of the research on OIE has been focused to the English language and little or no research has been conducted on other languages, including German. To tackle this problem, we developed GerIE, an OIE system for the German language. We surveyed the literature on OIE in order to identify concepts that may apply to the German language. Our system is based on the output of a German dependency parser and a number of handcrafted rules to extract the propositions. To
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Sena, Cleiton Fernando Lima, and Daniela Barreiro Claro. "InferPortOIE: A Portuguese Open Information Extraction system with inferences." Natural Language Engineering 25, no. 2 (2018): 287–306. http://dx.doi.org/10.1017/s135132491800044x.

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AbstractNowadays, there is an increasing amount of digital data. In the case of the Web, daily, a vast collection of data is generated, whose contents are heterogeneous. A significant portion of this data is available in a natural language format. Open Information Extraction (Open IE) enables the extraction of facts from large quantities of texts written in natural language. In this work, we propose an Open IE method to extract facts from texts written in Portuguese. We developed two new rules that generalize the inference by transitivity and by symmetry. Consequently, this approach increases
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Kadu, Payal. "Knowledge Extraction from Text Document Using Open Information Extraction Technique." International Journal of Advanced Trends in Computer Science and Engineering 9, no. 2 (2020): 2280–83. http://dx.doi.org/10.30534/ijatcse/2020/208922020.

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Han, Jiabao, and Hongzhi Wang. "Transformer based network for Open Information Extraction." Engineering Applications of Artificial Intelligence 102 (June 2021): 104262. http://dx.doi.org/10.1016/j.engappai.2021.104262.

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Dissertations / Theses on the topic "Open information extraction"

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Xavier, Clarissa Castell? "Learning non-verbal relations under open information extraction paradigm." Pontif?cia Universidade Cat?lica do Rio Grande do Sul, 2014. http://tede2.pucrs.br/tede2/handle/tede/5275.

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Made available in DSpace on 2015-04-14T14:50:19Z (GMT). No. of bitstreams: 1 466321.pdf: 1994049 bytes, checksum: fbbeef81814a876679c25f4e015925f5 (MD5) Previous issue date: 2014-03-12<br>O paradigma Open Information Extraction - Open IE (Extra??o Aberta de Informa??es) de extra??o de rela??es trabalha com a identifica??o de rela??es n?o definidas previamente, buscando superar as limita??es impostas pelos m?todos tradicionais de Extra??o de Informa??es como a depend?ncia de dom?nio e a dif?cil escalabilidade. Visando estender o paradigma Open IE para que sejam extra?das rela??es n?o expres
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Xavier, Clarissa Castellã. "Learning non-verbal relations under open information extraction paradigm." Pontifícia Universidade Católica do Rio Grande do Sul, 2014. http://hdl.handle.net/10923/7073.

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Made available in DSpace on 2015-03-17T02:01:01Z (GMT). No. of bitstreams: 1 000466321-Texto+Completo-0.pdf: 1994049 bytes, checksum: fbbeef81814a876679c25f4e015925f5 (MD5) Previous issue date: 2014<br>The Open Information Extraction (Open IE) is a relation extraction paradigm in which the target relationships cannot be specified in advance, and it aims to overcome the limitations imposed by traditional IE methods, such as domain-dependence and scalability. In order to extend Open IE to extract relationships that are not expressed by verbs from texts in English, we introduce CompIE, a compon
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Xavier, Clarissa [Verfasser]. "Learning Non-Verbal Relations Under Open Information Extraction Paradigm / Clarissa Xavier." Munich : GRIN Verlag, 2015. http://d-nb.info/1097578720/34.

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Gashteovski, Kiril [Verfasser], and Rainer [Akademischer Betreuer] Gemulla. "Compact open information extraction: methods, corpora, analysis / Kiril Gashteovski ; Betreuer: Rainer Gemulla." Mannheim : Universitätsbibliothek Mannheim, 2021. http://d-nb.info/123650285X/34.

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Genest, Pierre-Yves. "Unsupervised open-world information extraction from unstructured and domain-specific document collections." Electronic Thesis or Diss., Lyon, INSA, 2024. http://www.theses.fr/2024ISAL0111.

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La croissance exponentielle de la production de données a fait de l’analyse de collections de documents textuels non structurés un défi majeur. Cette thèse de doctorat vise à relever ce défi en se concentrant sur l’extraction d’information (IE), qui englobe quatre tâches principales : reconnaissance d’entités nommées (NER), résolution des coréférences (CR), annotation sémantique (EL) et extraction de relations (RE). Ces tâches permettent d’extraire et de structurer des connaissances à partir de documents non formatés, ce qui facilite leur intégration dans des bases de données structurées et le
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Ramsey, Marshall C., Thian-Huat Ong, and Hsinchun Chen. "Multilingual Input System for the Web - an Open Multimedia Approach of Keyboard and Handwriting Recognition for Chinese and Japanese." IEEE, 1998. http://hdl.handle.net/10150/105120.

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Artificial Intelligence Lab, Department of MIS, University of Arizona<br>The basic building block of a multilingual information retrieval system is the input system. Chinese and Japanese characters pose great challenges for the conventional 101 -key alphabet-based keyboard, because they are radical-based and number in the thousands. This paper reviews the development of various approaches and then presents a framework and working demonstrations of Chinese and Japanese input methods implemented in Java, which allow open deployment over the web to any platform, The demo includes both po
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Ramsey, Marshall C., Thian-Huat Ong, and Hsinchun Chen. "Multilingual input system for the Web - an open multimedia approach of keyboard and handwritten recognition for Chinese and Japanese." IEEE, 1998. http://hdl.handle.net/10150/105350.

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Artificial Intelligence Lab, Department of MIS, University of Arizona<br>The basic building block of a multilingual information retrieval system is the input system. Chinese and Japanese characters pose great challenges for the conventional 101-key alphabet-based keyboard, because they are radical-based and number in the thousands. This paper reviews the development of various approaches and then presents a framework and working demonstrations of Chinese and Japanese input methods implemented in Java, which allow open deployment over the web to any platform, The demo includes both pop
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De, Wilde Max. "From Information Extraction to Knowledge Discovery: Semantic Enrichment of Multilingual Content with Linked Open Data." Doctoral thesis, Universite Libre de Bruxelles, 2015. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/218774.

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Discovering relevant knowledge out of unstructured text in not a trivial task. Search engines relying on full-text indexing of content reach their limits when confronted to poor quality, ambiguity, or multiple languages. Some of these shortcomings can be addressed by information extraction and related natural language processing techniques, but it still falls short of adequate knowledge representation. In this thesis, we defend a generic approach striving to be as language-independent, domain-independent, and content-independent as possible. To reach this goal, we offer to disambiguate terms w
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FRESCHI, Sergio. "A Multidisciplinary Approach to the Reuse of Open Learning Resources." University of Sydney, 2008. http://hdl.handle.net/2123/3937.

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Master of Engineering (Research)<br>Educational standards are having a significant impact on e-Learning. They allow for better exchange of information among different organizations and institutions. They simplify reusing and repurposing learning materials. They give teachers the possibility of personalizing them according to the student’s background and learning speed. Thanks to these standards, off-the-shelf content can be adapted to a particular student cohort’s context and learning needs. The same course content can be presented in different languages. Overall, all the parties involved in t
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Del, Corro Luciano [Verfasser], and Rainer [Akademischer Betreuer] Gemulla. "Methods for open information extraction and sense disambiguation on natural language text / Luciano Del Corro. Betreuer: Rainer Gemulla." Saarbrücken : Saarländische Universitäts- und Landesbibliothek, 2016. http://d-nb.info/1081935006/34.

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Books on the topic "Open information extraction"

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Niklaus, Christina. From Complex Sentences to a Formal Semantic Representation using Syntactic Text Simplification and Open Information Extraction. Springer Fachmedien Wiesbaden, 2022. http://dx.doi.org/10.1007/978-3-658-38697-9.

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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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Xavier, Clarissa. Learning Non-Verbal Relations under Open Information Extraction Paradigm. GRIN Verlag GmbH, 2015.

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Niklaus, Christina. From Complex Sentences to a Formal Semantic Representation Using Syntactic Text Simplification and Open Information Extraction. Springer Fachmedien Wiesbaden GmbH, 2022.

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Harabagiu, Sanda, and Dan Moldovan. Question Answering. Edited by Ruslan Mitkov. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780199276349.013.0031.

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Textual Question Answering (QA) identifies the answer to a question in large collections of on-line documents. By providing a small set of exact answers to questions, QA takes a step closer to information retrieval rather than document retrieval. A QA system comprises three modules: a question-processing module, a document-processing module, and an answer extraction and formulation module. Questions may be asked about any topic, in contrast with Information Extraction (IE), which identifies textual information relevant only to a predefined set of events and entities. The natural language proce
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Arce, Moisés, Michael S. Hendricks, and Marc S. Polizzi. The Roots of Engagement. Oxford University PressNew York, 2022. http://dx.doi.org/10.1093/oso/9780197639672.001.0001.

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Abstract Studies of resource conflicts emphasize the structural characteristics of mining projects and the strategies of pro- and anti-mining groups in the context of large-scale mining. In this book, we take a different approach that looks at individuals living near proposed mines. We argue and show that individuals are drawn to their communities in different ways. Some of them participate in local organizations more than others, and this social engagement sets them apart from each other when it comes to their views and later demands about mining. By participating in local organizations, indi
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Book chapters on the topic "Open information extraction"

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Gamallo, Pablo, and Marcos Garcia. "Multilingual Open Information Extraction." In Progress in Artificial Intelligence. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23485-4_72.

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Li, Yucheng, Yan Yang, Qinmin Hu, Chengcai Chen, and Liang He. "An Argument Extraction Decoder in Open Information Extraction." In Lecture Notes in Computer Science. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72113-8_21.

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Lkhagvasuren, Ganchimeg, and Javkhlan Rentsendorj. "Open Information Extraction for Mongolian Language." In Advances in Intelligent Information Hiding and Multimedia Signal Processing. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9710-3_31.

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Dutta, Arnab, and Michael Schuhmacher. "Entity Linking for Open Information Extraction." In Natural Language Processing and Information Systems. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07983-7_11.

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Bast, Hannah, and Elmar Haussmann. "More Informative Open Information Extraction via Simple Inference." In Lecture Notes in Computer Science. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-06028-6_61.

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Pertsas, Vayianos, and Panos Constantopoulos. "Ontology-Driven Information Extraction from Research Publications." In Digital Libraries for Open Knowledge. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00066-0_21.

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Muhammad, Iqra, Anna Kearney, Carrol Gamble, Frans Coenen, and Paula Williamson. "Open Information Extraction for Knowledge Graph Construction." In Communications in Computer and Information Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59028-4_10.

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Siragusa, Giovanni, Rohan Nanda, Valeria De Paiva, and Luigi Di Caro. "Relating Legal Entities via Open Information Extraction." In Metadata and Semantic Research. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-14401-2_17.

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Li, Yang, Qingliang Miao, Tong Guo, Ji Geng, Changjian Hu, and Feiyu Xu. "Pattern Learning for Chinese Open Information Extraction." In Communications in Computer and Information Science. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-3146-6_7.

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Hassan, Ehab, Davide Buscaldi, and Aldo Gangemi. "Correlating Open Rating Systems and Event Extraction from Text." In Neural Information Processing. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-26561-2_44.

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Conference papers on the topic "Open information extraction"

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Li, Xiaozhe, Sihang Dang, Yifei Sun, Xiaoyue Jiang, Shuliang Gui, and Xiaoyi Feng. "Open-Set Remote Sensing Object Detection Using Edge Information Extraction." In 2024 IEEE International Conference on Signal, Information and Data Processing (ICSIDP). IEEE, 2024. https://doi.org/10.1109/icsidp62679.2024.10869041.

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Lamarche, Fabrice, and Philippe Langlais. "BenchIE^FL: A Manually Re-Annotated Fact-Based Open Information Extraction Benchmark." In Findings of the Association for Computational Linguistics ACL 2024. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.findings-acl.496.

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Cui, Lei, Furu Wei, and Ming Zhou. "Neural Open Information Extraction." In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). Association for Computational Linguistics, 2018. http://dx.doi.org/10.18653/v1/p18-2065.

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Yu, Bowen, Zhenyu Zhang, Jiawei Sheng, et al. "Semi-Open Information Extraction." In WWW '21: The Web Conference 2021. ACM, 2021. http://dx.doi.org/10.1145/3442381.3450029.

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Stanovsky, Gabriel, Julian Michael, Luke Zettlemoyer, and Ido Dagan. "Supervised Open Information 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/n18-1081.

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Truong, Diem, Duc-Thuen Vo, and Uyen Trang Nguyen. "Vietnamese Open Information Extraction." In SoICT 2017: The Eighth International Symposium on Information and Communication Technology. ACM, 2017. http://dx.doi.org/10.1145/3155133.3155171.

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Pei, Kevin, Ishan Jindal, and Kevin Chang. "Abstractive Open Information Extraction." In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2023. http://dx.doi.org/10.18653/v1/2023.emnlp-main.376.

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Yu, Bowen, Zhenyu Zhang, Jingyang Li, et al. "Towards Generalized Open Information Extraction." In Findings of the Association for Computational Linguistics: EMNLP 2022. Association for Computational Linguistics, 2022. http://dx.doi.org/10.18653/v1/2022.findings-emnlp.103.

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Dong, Kuicai, Aixin Sun, Jung-jae Kim, and Xiaoli Li. "Open Information Extraction via Chunks." In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2023. http://dx.doi.org/10.18653/v1/2023.emnlp-main.951.

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Paşca, Marius. "Web-based open-domain information extraction." In the 20th international conference companion. ACM Press, 2011. http://dx.doi.org/10.1145/1963192.1963319.

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Reports on the topic "Open information extraction"

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Etzioni, Oren. Open Information Extraction. Defense Technical Information Center, 2010. http://dx.doi.org/10.21236/ada538482.

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Hsieh, Patrick, Eric Apaydin, Robert G. Briggs, et al. Diagnosis and Treatment of Tethered Spinal Cord. Agency for Healthcare Research and Quality (AHRQ), 2024. http://dx.doi.org/10.23970/ahrqepccer274.

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Objectives. To summarize the evidence regarding diagnosis, prophylactic treatment, symptomatic treatment, and repeat surgery of tethered spinal cord. Data sources. We searched PubMed®, Embase®, CINAHL, Web of Science, SCOPUS, clinicaltrials.gov, ICTRP, Cochrane Database of Systematic Reviews, PROSPERO, ECRI repository, G-I-N, MagicApp, and ClinicalKey from inception to March 2024; reference-mined reviews; and contacted research authors. Review methods. The review followed a detailed protocol and was supported by a Technical Expert Panel. Systematic review software (DistillerSR) was utilized fo
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Andresen, Jens-Bjørn R., and Søren M. Kristiansen. Historic maps as source for hydrological reconstruction of pre-industrial landscape wetness in Denmark: a methodological study. Det Kgl. Bibliotek, 2023. http://dx.doi.org/10.7146/aul.491.

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Historic maps are an important primary source which can be utilized in the reconstruction of environmental variables of the pre-industrial landscape. However, methodological constraints have hitherto prevented large scale and systematic approaches. In this paper a novel methodology is presented, which documents the usefulness of the maps in the study of paleo-hydrology and thus serves a better understanding of the conditions for agricultural production under pre-drainage conditions. The methodology is developed based on eighteenth and nineteenth century maps from a 100 km2 study area in one st
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