Academic literature on the topic 'Multilingual information extraction'
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Journal articles on the topic "Multilingual information extraction"
Claro, Daniela Barreiro, Marlo Souza, Clarissa Castellã Xavier, and Leandro Oliveira. "Multilingual Open Information Extraction: Challenges and Opportunities." Information 10, no. 7 (July 2, 2019): 228. http://dx.doi.org/10.3390/info10070228.
Full textKhairova, Nina, Orken Mamyrbayev, Kuralay Mukhsina, Anastasiia Kolesnyk, and Saurabh Pratap. "Logical-linguistic model for multilingual Open Information Extraction." Cogent Engineering 7, no. 1 (January 1, 2020): 1714829. http://dx.doi.org/10.1080/23311916.2020.1714829.
Full textHashemzahde, Bahare, and Majid Abdolrazzagh-Nezhad. "Improving keyword extraction in multilingual texts." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 6 (December 1, 2020): 5909. http://dx.doi.org/10.11591/ijece.v10i6.pp5909-5916.
Full textVasilkovsky, Michael, Anton Alekseev, Valentin Malykh, Ilya Shenbin, Elena Tutubalina, Dmitriy Salikhov, Mikhail Stepnov, Andrey Chertok, and Sergey Nikolenko. "DetIE: Multilingual Open Information Extraction Inspired by Object Detection." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 10 (June 28, 2022): 11412–20. http://dx.doi.org/10.1609/aaai.v36i10.21393.
Full textGhimire, Dadhi Ram, Sanjeev Panday, and Aman Shakya. "Information Extraction from a Large Knowledge Graph in the Nepali Language." National College of Computer Studies Research Journal 3, no. 1 (December 9, 2024): 33–49. https://doi.org/10.3126/nccsrj.v3i1.72336.
Full textAzzam, Saliha, Kevin Humphreys, Robert Gaizauskas, and Yorick Wilks. "Using a language independent domain model for multilingual information extraction." Applied Artificial Intelligence 13, no. 7 (October 1999): 705–24. http://dx.doi.org/10.1080/088395199117252.
Full textSeretan, Violeta, and Eric Wehrli. "Multilingual collocation extraction with a syntactic parser." Language Resources and Evaluation 43, no. 1 (October 1, 2008): 71–85. http://dx.doi.org/10.1007/s10579-008-9075-7.
Full textZhang, Ruijuan. "Multilingual pretrained based multi-feature fusion model for English text classification." Computer Science and Information Systems, no. 00 (2025): 4. https://doi.org/10.2298/csis240630004z.
Full textDanielsson, Pernilla. "Automatic extraction of meaningful units from corpora." International Journal of Corpus Linguistics 8, no. 1 (August 14, 2003): 109–27. http://dx.doi.org/10.1075/ijcl.8.1.06dan.
Full textAysa, Anwar, Mijit Ablimit, Hankiz Yilahun, and Askar Hamdulla. "Chinese-Uyghur Bilingual Lexicon Extraction Based on Weak Supervision." Information 13, no. 4 (March 31, 2022): 175. http://dx.doi.org/10.3390/info13040175.
Full textDissertations / Theses on the topic "Multilingual information extraction"
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.
Full textThe 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 popular keyboard input methods and neural network handwriting recognition using a mouse or pen. This framework is able to accommodate future extension to other input mediums and languages of interest.
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.
Full textThe 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 popular keyboard input methods and neural network handwriting recognition using a mouse or pen. This framework is able to accommodate future extension to other input mediums and languages of interest.
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.
Full textDécouvrir de nouveaux savoirs dans du texte non-structuré n'est pas une tâche aisée. Les moteurs de recherche basés sur l'indexation complète des contenus montrent leur limites quand ils se voient confrontés à des textes de mauvaise qualité, ambigus et/ou multilingues. L'extraction d'information et d'autres techniques issues du traitement automatique des langues permettent de répondre partiellement à cette problématique, mais sans pour autant atteindre l'idéal d'une représentation adéquate de la connaissance. Dans cette thèse, nous défendons une approche générique qui se veut la plus indépendante possible des langues, domaines et types de contenus traités. Pour ce faire, nous proposons de désambiguïser les termes à l'aide d'identifiants issus de bases de connaissances du Web des données, facilitant ainsi l'enrichissement sémantique des contenus. La valeur ajoutée de cette approche est illustrée par une étude de cas basée sur une archive historique trilingue, en mettant un accent particulier sur les contraintes de qualité, de multilinguisme et d'évolution dans le temps. Un prototype d'outil est également développé sous le nom de Multilingual Entity/Resource Combiner & Knowledge eXtractor (MERCKX), démontrant ainsi le caractère généralisable de notre approche, dans un certaine mesure, à n'importe quelle langue, domaine ou type de contenu.
Doctorat en Information et communication
info:eu-repo/semantics/nonPublished
Schleider, Thomas. "Knowledge Modeling and Multilingual Information Extraction for the Understanding of the Cultural Heritage of Silk." Electronic Thesis or Diss., Sorbonne université, 2022. http://www.theses.fr/2022SORUS280.
Full textModeling any type of human knowledge is a complex effort and needs to consider all specificities of its domain including niche vocabulary. This thesis focuses on such an endeavour for the knowledge about the European silk object production, which can be considered obscure and therefore endangered. However, the fact that such Cultural Heritage data is heterogenous, spread across many museums worldwide, sparse and multilingual poses particular challenges for which knowledge graphs have become more and more popular in recent years. Our main goal is not only into investigating knowledge representations, but also in which ways such an integration process can be accompanied through enrichments, such as information reconciliation through ontologies and vocabularies, as well as metadata predictions to fill gaps in the data. We will first propose a workflow for the management for the integration of data about silk artifacts and afterwards present different classification approaches, with a special focus on unsupervised and zero-shot methods. Finally, we study ways of making exploration of such metadata and images afterwards as easy as possible
Yeh, Hui-Syuan. "Prompt-based Relation Extraction for Pharmacovigilance." Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASG097.
Full textExtracting and maintaining up-to-date knowledge from diverse linguistic sources is imperative for the benefit of public health. While professional sources, including scientific journals and clinical notes, provide the most reliable knowledge, observations reported in patient forums and social media can bring complementary information for certain themes. Spotting entities and their relationships in these varied sources is particularly valuable. We focus on relation extraction in the medical domain. At the outset, we highlight the inconsistent terminology in the community and clarify the diverse setups used to build and evaluate relation extraction systems. To obtain reliable comparisons, we compare systems using the same setup. Additionally, we conduct a series of stratified evaluations to further investigate which data properties affect the models' performance. We show that model performance tends to decrease with relation density, relation diversity, and entity distance. Subsequently, this work explores a new training paradigm for biomedical relation extraction: prompt-based methods with masked language models. In this context, performance depends on the quality of prompt design. This requires manual efforts and domain knowledge, especially when designing the label words that link model predictions to relation classes. To overcome this overhead, we introduce an automated label word generation technique leveraging a dependency parser and training data. This approach minimizes manual intervention and enhances model performance with fewer parameters to be fine-tuned. Our approach performs on par with other verbalizer methods without additional training. Then, this work addresses information extraction from text written by laypeople about adverse drug reactions. To this end, as part of a joint effort, we have curated a tri-lingual corpus in German, French, and Japanese collected from patient forums and social media platforms. The challenge and the potential applications of the corpus are discussed. We present baseline experiments on the corpus that highlight three points: the effectiveness of a multilingual model in the cross-lingual setting, preparing negative samples for relation extraction by considering the co-reference and the distance between entities, and methods to address the highly imbalanced distribution of relations. Lastly, we integrate information from a medical knowledge base into the prompt-based approach with autoregressive language models for biomedical relation extraction. Our goal is to use external factual knowledge to enrich the context of the entities involved in the relation to be classified. We find that general models particularly benefit from external knowledge. Our experimental setup reveals that different entity markers are effective across different corpora. We show that the relevant knowledge helps, though the format of the prompt has a greater impact on performance than the additional information itself
Akbik, Alan [Verfasser], Volker [Akademischer Betreuer] Markl, Hans [Gutachter] Uszkoreit, and Chris [Gutachter] Biemann. "Exploratory relation extraction in large multilingual data / Alan Akbik ; Gutachter: Hans Uszkoreit, Chris Biemann ; Betreuer: Volker Markl." Berlin : Technische Universität Berlin, 2016. http://d-nb.info/1156177308/34.
Full textGuénec, Nadège. "Méthodologies pour la création de connaissances relatives au marché chinois dans une démarche d'Intelligence Économique : application dans le domaine des biotechnologies agricoles." Phd thesis, Université Paris-Est, 2009. http://tel.archives-ouvertes.fr/tel-00554743.
Full textCharton, Éric. "Génération de phrases multilingues par apprentissage automatique de modèles de phrases." Thesis, Avignon, 2010. http://www.theses.fr/2010AVIG0175/document.
Full textNatural Language Generation (NLG) is the natural language processing task of generating natural language from a machine representation system. In this thesis report, we present an architecture of NLG system relying on statistical methods. The originality of our proposition is its ability to use a corpus as a learning resource for sentences production. This method offers several advantages : it simplifies the implementation and design of a multilingual NLG system, capable of sentence production of the same meaning in several languages. Our method also improves the adaptability of a NLG system to a particular semantic field. In our proposal, sentence generation is achieved trough the use of sentence models, obtained from a training corpus. Extracted sentences are abstracted by a labelling step obtained from various information extraction and text mining methods like named entity recognition, co-reference resolution, semantic labelling and part of speech tagging. The sentence generation process is achieved by a sentence realisation module. This module provide an adapted sentence model to fit a communicative intent, and then transform this model to generate a new sentence. Two methods are proposed to transform a sentence model into a generated sentence, according to the semantic content to express. In this document, we describe the complete labelling system applied to encyclopaedic content to obtain the sentence models. Then we present two models of sentence generation. The first generation model substitute the semantic content to an original sentence content. The second model is used to find numerous proto-sentences, structured as Subject, Verb, Object, able to fit by part a whole communicative intent, and then aggregate all the selected proto-sentences into a more complex one. Our experiments of sentence generation with various configurations of our system have shown that this new approach of NLG have an interesting potential
Gerber, Daniel [Verfasser], Klaus-Peter [Akademischer Betreuer] Fähnrich, Klaus-Peter [Gutachter] Fähnrich, Ngomo Axel-Cyrille [Akademischer Betreuer] Ngonga, and Axel [Gutachter] Polleres. "Statistical Extraction of Multilingual Natural Language Patterns for RDF Predicates: Algorithms and Applications / Daniel Gerber ; Gutachter: Klaus-Peter Fähnrich, Axel Polleres ; Klaus-Peter Fähnrich, Axel-Cyrille Ngonga Ngomo." Leipzig : Universitätsbibliothek Leipzig, 2016. http://d-nb.info/1239739478/34.
Full textBooks on the topic "Multilingual information extraction"
Poibeau, Thierry, Horacio Saggion, Jakub Piskorski, and Roman Yangarber, eds. Multi-source, Multilingual Information Extraction and Summarization. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-28569-1.
Full textGeoff, Barnbrook, Danielsson Pernilla, and Mahlberg Michaela, eds. Meaningful texts: The extraction of semantic information from monolingual and multilingual corpora. London: Continuum, 2005.
Find full textMultisource Multilingual Information Extraction And Summarization. Springer, 2012.
Find full textPoibeau, Thierry, Horacio Saggion, Jakub Piskorski, and Roman Yangarber. Multi-Source, Multilingual Information Extraction and Summarization. Springer London, Limited, 2012.
Find full textPoibeau, Thierry, Horacio Saggion, Jakub Piskorski, and Roman Yangarber. Multi-source, Multilingual Information Extraction and Summarization. Springer, 2014.
Find full textPoibeau, Thierry, Horacio Saggion, and Jakub Piskorski. Multi-source, Multilingual Information Extraction and Summarization. Springer, 2012.
Find full textMeaningful Texts: The Extraction of Semantic Information from Monolingual and Multilingual Corporations. Univ of Birmingham, 2004.
Find full textMeaningful Texts: The Extraction of Semantic Information from Monolingual and Multilingual Corporations. Univ of Birmingham, 2005.
Find full textDanielsson, Pernilla. Meaningful Texts: The Extraction of Semantic Information from Monolingual and Multilingual Corpora. Bloomsbury Publishing Plc, 2010.
Find full textDanielsson, Pernilla. Meaningful Texts: The Extraction of Semantic Information from Monolingual and Multilingual Corpora. Bloomsbury Publishing Plc, 2004.
Find full textBook chapters on the topic "Multilingual information extraction"
Gamallo, Pablo, and Marcos Garcia. "Multilingual Open Information Extraction." In Progress in Artificial Intelligence, 711–22. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23485-4_72.
Full textEsuli, Andrea, and Fabrizio Sebastiani. "Evaluating Information Extraction." In Multilingual and Multimodal Information Access Evaluation, 100–111. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15998-5_12.
Full textPalmer, David D., Marc B. Reichman, and Noah White. "Multimedia Information Extraction in a Live Multilingual News Monitoring System." In Multimedia Information Extraction, 145–57. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781118219546.ch9.
Full textKabadjov, Mijail, Josef Steinberger, and Ralf Steinberger. "Multilingual Statistical News Summarization." In Multi-source, Multilingual Information Extraction and Summarization, 229–52. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28569-1_11.
Full textThurmair, Gregor. "Multiword expressions in multilingual information extraction." In Multiword Units in Machine Translation and Translation Technology, 104–23. Amsterdam: John Benjamins Publishing Company, 2018. http://dx.doi.org/10.1075/cilt.341.05thu.
Full textDini, Luca. "Parallel Information Extraction System for Multilingual Information Access." In Advances in Intelligent Systems, 179–90. Dordrecht: Springer Netherlands, 1999. http://dx.doi.org/10.1007/978-94-011-4840-5_16.
Full textPiskorski, Jakub, and Roman Yangarber. "Information Extraction: Past, Present and Future." In Multi-source, Multilingual Information Extraction and Summarization, 23–49. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28569-1_2.
Full textRibeiro, Ricardo, and David Martins de Matos. "Improving Speech-to-Text Summarization by Using Additional Information Sources." In Multi-source, Multilingual Information Extraction and Summarization, 277–97. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28569-1_13.
Full textJi, Heng, Benoit Favre, Wen-Pin Lin, Dan Gillick, Dilek Hakkani-Tur, and Ralph Grishman. "Open-Domain Multi-Document Summarization via Information Extraction: Challenges and Prospects." In Multi-source, Multilingual Information Extraction and Summarization, 177–201. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28569-1_9.
Full textLi, Fang, Huanye Sheng, Dongmo Zhang, and Tianfang Yao. "An Internet Based Multilingual Investment Information Extraction System." In The Internet Challenge: Technology and Applications, 1–9. Dordrecht: Springer Netherlands, 2002. http://dx.doi.org/10.1007/978-94-010-0494-7_1.
Full textConference papers on the topic "Multilingual information extraction"
Sanjaya, Hafidz, Kusrini Kusrini, Kumara Ari Yuana, and José Ramén Martínez Salio. "Multilingual Named Entity Recognition Model for Location and Time Extraction of Forest Fire." In 2024 4th International Conference of Science and Information Technology in Smart Administration (ICSINTESA), 611–15. IEEE, 2024. http://dx.doi.org/10.1109/icsintesa62455.2024.10747844.
Full textYuan, Yue, and Huaping Zhang. "An Improved Topic Extraction Method Based on Word Frequency Information Entropy for Multilingual Topic Attentional Division." In 2024 9th International Conference on Intelligent Computing and Signal Processing (ICSP), 675–81. IEEE, 2024. http://dx.doi.org/10.1109/icsp62122.2024.10743506.
Full textWiedemann, Gregor, Seid Muhie Yimam, and Chris Biemann. "A Multilingual Information Extraction Pipeline for Investigative Journalism." In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations. Stroudsburg, PA, USA: Association for Computational Linguistics, 2018. http://dx.doi.org/10.18653/v1/d18-2014.
Full textKotnis, Bhushan, Kiril Gashteovski, Daniel Rubio, Ammar Shaker, Vanesa Rodriguez-Tembras, Makoto Takamoto, Mathias Niepert, and Carolin Lawrence. "MILIE: Modular & Iterative Multilingual Open Information Extraction." In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Stroudsburg, PA, USA: Association for Computational Linguistics, 2022. http://dx.doi.org/10.18653/v1/2022.acl-long.478.
Full textVijayan, Karthika, and Oshin Anand. "Language-Agnostic Text Processing for Information Extraction." In 12th International Conference on Artificial Intelligence, Soft Computing and Applications. Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.122310.
Full textAone, Chinatsu, Nicholas Charocopos, and James Gorlinsky. "An intelligent multilingual information browsing and retrieval system using information extraction." In the fifth conference. Morristown, NJ, USA: Association for Computational Linguistics, 1997. http://dx.doi.org/10.3115/974557.974606.
Full textMaynard, Diana, and Hamish Cunningham. "Multilingual adaptations of ANNIE, a reusable information extraction tool." In the tenth conference. Morristown, NJ, USA: Association for Computational Linguistics, 2003. http://dx.doi.org/10.3115/1067737.1067789.
Full textKolluru, Keshav, Muqeeth Mohammed, Shubham Mittal, Soumen Chakrabarti, and Mausam . "Alignment-Augmented Consistent Translation for Multilingual Open Information Extraction." In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Stroudsburg, PA, USA: Association for Computational Linguistics, 2022. http://dx.doi.org/10.18653/v1/2022.acl-long.179.
Full textBretschneider, Claudia, Heiner Oberkampf, Sonja Zillner, Bernhard Bauer, and Matthias Hammon. "Corpus-based Translation of Ontologies for Improved Multilingual Semantic Annotation." In Proceedings of the Third Workshop on Semantic Web and Information Extraction. Stroudsburg, PA, USA: Association for Computational Linguistics and Dublin City University, 2014. http://dx.doi.org/10.3115/v1/w14-6201.
Full textNguyen, Minh Van, Nghia Ngo, Bonan Min, and Thien Nguyen. "FAMIE: A Fast Active Learning Framework for Multilingual Information Extraction." In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: System Demonstrations. Stroudsburg, PA, USA: Association for Computational Linguistics, 2022. http://dx.doi.org/10.18653/v1/2022.naacl-demo.14.
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