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Learning Disentangled Representations of Negation and Uncertainty ...
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Natural language processing applied to mental illness detection: a narrative review
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In: NPJ Digit Med (2022)
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Towards BERT-based Automatic ICD Coding: Limitations and Opportunities
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In: Proceedings of the 20th Workshop on Biomedical Language Processing (2021)
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BioVAE: a pre-trained latent variable language model for biomedical text mining
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In: Bioinformatics (2021)
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Hypothesis, analysis and synthesis: it’s all Greek to me! ...
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Hypothesis, analysis and synthesis: it’s all Greek to me! ...
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Hypothesis, analysis and synthesis: it’s all Greek to me! ...
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Modelling Instance-Level Annotator Reliability for Natural Language Labelling Tasks ...
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Inter-sentence Relation Extraction with Document-level Graph Convolutional Neural Network ...
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Hypothesis, analysis and synthesis: it’s all Greek to me! ...
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Improving clinical named entity recognition in Chinese using the graphical and phonetic feature
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Identification of research hypotheses and new knowledge from scientific literature ...
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Identification of research hypotheses and new knowledge from scientific literature ...
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Abstract:
Abstract Background Text mining (TM) methods have been used extensively to extract relations and events from the literature. In addition, TM techniques have been used to extract various types or dimensions of interpretative information, known as Meta-Knowledge (MK), from the context of relations and events, e.g. negation, speculation, certainty and knowledge type. However, most existing methods have focussed on the extraction of individual dimensions of MK, without investigating how they can be combined to obtain even richer contextual information. In this paper, we describe a novel, supervised method to extract new MK dimensions that encode Research Hypotheses (an authorâ s intended knowledge gain) and New Knowledge (an authorâ s findings). The method incorporates various features, including a combination of simple MK dimensions. Methods We identify previously explored dimensions and then use a random forest to combine these with linguistic features into a classification model. To facilitate evaluation of ...
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Keyword:
19999 Mathematical Sciences not elsewhere classified; 69999 Biological Sciences not elsewhere classified; 80699 Information Systems not elsewhere classified; Cancer; FOS Biological sciences; FOS Computer and information sciences; FOS Mathematics; Science Policy
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URL: https://figshare.com/collections/Identification_of_research_hypotheses_and_new_knowledge_from_scientific_literature/4145369/1 https://dx.doi.org/10.6084/m9.figshare.c.4145369.v1
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Hypothesis, analysis and synthesis: it’s all Greek to me! ...
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Distributed Document and Phrase Co-embeddings for Descriptive Clustering
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