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Exploring Construction of a Company Domain-Specific Knowledge Graph from Financial Texts Using Hybrid Information Extraction
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Jen, Chun-Heng. - : KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021
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Analyzing Non-Textual Content Elements to Detect Academic Plagiarism
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Multi-Word Terminology Extraction and Its Role in Document Embedding
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In: Electronic Theses and Dissertations (2021)
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Linguistic Analysis and Automatic Information Extraction of Semantic Relations in Arabic ; Analyse linguistique et extraction automatique de relations sémantiques des textes en arabe
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In: https://hal.archives-ouvertes.fr/tel-03572307 ; Linguistique. Université Bourgogne Franche-Comté, 2020. Français (2020)
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Prerequisites for Extracting Entity Relations from Swedish Texts
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Lenas, Erik. - : KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020
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Focus Particles and Extraction – An Experimental Investigation of German and English Focus Particles in Constructions with Leftward Association
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Extracting Global Entities Information from News
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Xia, Chen. - : eScholarship, University of California, 2019
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In: Xia, Chen. (2019). Extracting Global Entities Information from News. UCLA: Computer Science 0201. Retrieved from: http://www.escholarship.org/uc/item/0bv836gm (2019)
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NormCo: Deep Disease Normalization for Biomedical Knowledge Base Construction
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In: Wright, Dustin. (2019). NormCo: Deep Disease Normalization for Biomedical Knowledge Base Construction. UC San Diego: Computer Science and Engineering. Retrieved from: http://www.escholarship.org/uc/item/3410q7zk (2019)
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Biomedical Literature Mining and Knowledge Discovery of Phenotyping Definitions
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Contextual citation recommendation using scientific discourse annotation schemes
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Informationsextraktion aus Wirtschaftsnachrichten über Unternehmenszusammenschlüsse mit lokalen Grammatiken
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IDS Mannheim
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Personnalisation et enrichissement des méthodes d’accès aux données
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In: https://hal.inria.fr/tel-01739707 ; Base de données [cs.DB]. Université Rennes 1, 2018 (2018)
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SMS communication : Natural language processing and information extraction ; Communiquer par SMS : Analyse automatique du langage et extraction de l'information véhiculée
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In: https://tel.archives-ouvertes.fr/tel-01968698 ; Linguistique. Université Grenoble Alpes, 2018. Français. ⟨NNT : 2018GREAL012⟩ (2018)
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Deep Text Mining of Instagram Data Without Strong Supervision ; Textutvinning från Instagram utan Precis Övervakning
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Hammar, Kim. - : KTH, Programvaruteknik och datorsystem, SCS, 2018
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A Step Toward GDPR Compliance : Processing of Personal Data in Email
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Building effective representations for domain adaptation in coreference resolution
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Abstract:
Over the past few years, research in coreference resolution, one of the core tasks in Natural Language processing, has displayed significant improvement. However, the field of domain adaptation in coreference resolution is yet to be explored; Moosavi and Strube [2017] have shown that the performance of state-of-the-art coreference resolution systems drop when the systems are tested on datasets from different domains. We modify e2e-coref [Lee et al., 2017], a state-of-the-art coreference resolution system, to perform well on new domains by adding sparse linguistic features, incorporating information from Wikipedia, and implementing a domain adversarial network to the system. Our experiments show that each modification improves the precision of the system. We train the model on CoNLL-2012 datasets and test it on several datasets: WikiCoref, the pt documents, and the wb documents from CoNLL-2012. Our best results gains 0.50, 0.52, and 1.14 F1 improvements over the baselines of the respective test sets. ; Computer Sciences
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Keyword:
Coreference resolution; Domain adaptation; Domain adversarial; Information extraction; Natural language processing; Neural network; Wikipedia
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URL: http://hdl.handle.net/2152/68211 https://doi.org/10.15781/T2NP1X34P
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Analysis of social health media to assess the quality of life of breast cancer patients ; Analyse des médias sociaux de santé pour évaluer la qualité de vie des patientes atteintes d’un cancer du sein
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In: https://tel.archives-ouvertes.fr/tel-01919773 ; Autres [stat.ML]. Université Montpellier, 2017. Français. ⟨NNT : 2017MONTS039⟩ (2017)
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